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Advanced differential interferometrysynthetic aperture radar techniquesfor deformation monitoring a reviewon sensors and recent researchdevelopmentOIdrees Mohammed a Vahideh Saeidi a Biswajeet Pradhan a ampYusuf Ahmed Yusufa Faculty of Engineering Department of Civil Engineering University Putra Malaysia Serdang Selangor Darul Ehsan 43400UPM MalaysiaAccepted author version posted online 24 May 2013Publishedonline 01 Jul 2013

To cite this article Geocarto International (2013) Advanced differential interferometry syntheticaperture radar techniques for deformation monitoring a review on sensors and recent researchdevelopment Geocarto International DOI 101080101060492013807305

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Advanced differential interferometry synthetic aperture radartechniques for deformation monitoring a review on sensors and

recent research development

OIdrees Mohammed Vahideh Saeidi Biswajeet Pradhan and Yusuf Ahmed Yusuf

Faculty of Engineering Department of Civil Engineering University Putra Malaysia SerdangSelangor Darul Ehsan 43400 UPM Malaysia

(Received 25 January 2013 final version received 6 May 2013)

This paper reviews the advanced differential interferometry synthetic aperture radar(A-DInSAR) techniques with two major components in focus First is the basicconcepts synthetic aperture radar (SAR) data sources and the different algorithmsdocumented in the literature primarily focusing on persistent scatterers In the sec-ond part the techniques are compared in order to establish more linkage in terms ofthe variability of their applications strength and validation of the interpreted resultsAlso current issues in sensor and algorithm development are discussed The studyidentified six existing A-DInSAR algorithms used for monitoring various deforma-tion types Generally reports of their performance indicate that all the techniques arecapable of measuring deformation phenomena at varying spatial resolution with highlevel of accuracy However their usability in suburban and vegetated areas yieldspoor results compared to urbanized areas due to inadequate permanent features thatcould provide sufficient coherent point targets Meanwhile there is continuous devel-opment in sensors and algorithms to expand the applicability domain of the technol-ogy for a wide range of deformable surfaces and displacement patterns with higherprecision On the sensor side most of the latest SAR sensors employ longer wave-length (X and P bands) to increase the penetrating power of the signal and two othersensors (ALOS-2 PALSA-2 and SENTINEL-1) are scheduled to be launched in2013 Researchers are investigating the possibility of using single-pass sensors withdifferent look angles for SAR data collection With these it is expected that moredata will be available for various applications Algorithms such as corner reflectorinterferometry SAR along track interferometry liqui-InSAR and squeeSAR areemerging to increase reliable estimation of deformation from different surfaces

Keywords interferometry DInSAR surface deformation algorithm satellite sensorvalidation

1 Introduction

Synthetic aperture radar (SAR) is a major advance in radar remote sensing thatsynthesizes a long antenna to improve azimuth resolution The use of two SAR imagesacquired over the same area at different time or slightly different view angles togenerate maps of surface deformation or digital elevation by exploiting the differencesin the phase of the waves returning to the radar sensor is called interferometry SAR

Corresponding author Email biswajeetengupmedumy

Geocarto International 2013httpdxdoiorg101080101060492013807305

2013 Taylor amp Francis

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(InSAR) (Kenyi amp Kaufmann 2003 Caro et al 2011) Figure 1 illustrates the SAR datacollection scheme

SAR images from satellite sensors were first experimented for deformation studiesin 1991 following the launch of ERS-1 by European Space Agency (ESA) (LanariCasu Manzo Zeni et al 2007 Michele et al 2008) Since then the technique hadbeen widely employed and confirmed to be a useful and powerful tool for monitoringsubtle surface deformation related to geodynamic phenomena over a long period oftime providing results with millimetre-level accuracy (Ferretti et al 2005 Gini ampLombardini 2005 Lanari Casu Manzo Zeni et al 2007 Herrera et al 2009 Tarikhi2010 Marghany 2011) In principle InSAR relies on the ability to measure differencein phase of two or more images to extract information about topography temporal sta-bility and deformation velocity (Kenyi amp Kaufmann 2003 Kampes amp Hanssen 2004Bamler et al 2005 Lee et al 2005 Lu amp Liao 2008) For topographical informationextraction digital elevation model (DEM) can be generated from two SAR imagestaken at the same position or slightly different positions with different view angles (spa-tial baseline) whereas deformation monitoring requires SAR images taken at differenttime intervals called temporal baseline (Canisius et al 2003 Bamler et al 2005Wegmuumlller et al 2009 Tarikhi 2010)

The investigation of the spatial and temporal pattern of ground deformation byanalysing a single interferogram that is derived from a pair of SAR images with the addi-tion of a DEM is called differential interferometry SAR (DInSAR) (Crosetto amp Crippa2005) Figure 2 illustrates the schematic concept of DInSAR for deformation measure-ment The DEM can be generated from global positioning system measurement digitaltopographic data or from interferometry derived from image pairs with short temporalbaseline and similar imaging geometry (Arjona Santoyo et al 2010 Tarikhi 2010)

Standard DInSAR configuration is flexible in providing qualitative information onthe deformation from two interferograms or a single interferogram and DEM Howeveratmospheric disturbance temporal decorrelation and inaccuracies of the external DEMconstitute major drawbacks that limit its operational capability (Cascini et al 2010Allen et al 2013) The foregoing limitations the need to quantify deformationphenomena in time series and the potentials to use available stacks of SAR images

Figure 1 DInSAR data collection schemeSource Tarikhi (2010)

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collected from different sensors lead to the development of several advanced DInSARalgorithms (Michele et al 2008 Bhattacharya et al 2012 Calograve 2012) For the past13 years since the first differential interferometric SAR technique was developed (Fer-retti et al 2000 2005 Rosen et al 2000) researchers in the field of remote sensinghave been exploring various techniques to optimally measure deformation parametersfrom SAR data

The international literature is overwhelmed with reports of the successful applica-tions of advanced DInSAR algorithms to deformation monitoring However it is nolonger sufficient to lay much emphasis on the relatively high accuracy (millimetre-level)obtainable without a valid assessment of the challenges faced by researchers and usersof the products of A-DInSAR techniques Such challenges include the need to knowwhat particular deformation phenomenon a specific algorithm can suitably be appliedto Most of the literature on DInSAR for deformation studies revealed that the tech-niques perform well in urban and semi-urban and poorly in vegetated areas glacierand water bodies Also some of the papers discuss on various efforts made to improvethe performance (Kampes amp Hanssen 2004 Werner et al 2005 Cascini et al 2010Fan et al 2010 Tarikhi 2011 2012) Apart from this not much work had been reportedon the comparative analysis of the strengths and limitations of the techniques includingvalidation methods of interpretation and confident use of the result

In this study we reviewed the state-of-the-art of the established DInSAR techniquesfor deformation studies and current developments To do this we conducted a meta-analysis of literature to (i) investigate the deformation phenomenon that had been

Phase Denoising

Coregistration

SAR Simulation

Topographic PhaseSimulation

Differential Interferogram Generation

External DEM

Average IntensityGeneration

Oversample

Master Image

Coregistration

Slave Image

Interferogram Generation

Coherent Map Generation

Phase Unwrapping

Phase to Displacement Conversion

Geocoding

Figure 2 Schematic concept of DInSAR for deformation measurementSource Ng (2010)

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reported understanding what deformation phenomenon each is suitable for and whypoor performance in some areas (ii) expand knowledge on comparative analysis of thestrengths and weaknesses of each technique and (iii) stimulate readers to further theprogression of the diagnostic techniques for validating and interpreting DInSAR resultsOur focus in this review is limited to space-borne SAR sensors therefore airborneSAR platform and their applications will not be discussed The rest of this paper isorganised as follows Section 2 gives a brief description of persistent scatterer interfer-ometry (PSI) explaining the basic fundamentals and the criteria for identifying coherentpoints In Section 3 we have reviewed prominent A-DInSAR techniques with highlightof the basic features peculiar to each of those techniques Sources of data and the avail-able satellite sensors for SAR data collection are explicitly tabulated in Section 4 Dis-cussion of our findings is the focus of Section 5 while Section 6 gives the review ofcurrent issue Finally Section 7 draws the closing remark with the conclusion

2 Persistent Scatterer Interferometry

Persistent scatterers (PS) are pixels that remain consistent for years in a series of SARimages collected over an area with the same sensor (Ferretti et al 2000 2005 Caroet al 2011) PSI relies on identifying point targets which remain coherent in order toselect networks of points against which precise deformation velocity measurement canbe made Point targets are imaged cells exhibiting dominant scattering by a targetusually smaller in size than the resolution cell Figure 3 shows retrieved scatterssuperimposed on aerial photographs indicating urban features that exhibit persistentpoint targets They do not show speckle characteristics common to distributed targets(Wang et al 2008) The advantage of PS technique is that it forestalls the problem of

Figure 3 Permanent scatterers superimposed on aerial photograph of New OrleansSource httplabscasusfedugeodesysarhtml

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(InSAR) (Kenyi amp Kaufmann 2003 Caro et al 2011) Figure 1 illustrates the SAR datacollection scheme

SAR images from satellite sensors were first experimented for deformation studiesin 1991 following the launch of ERS-1 by European Space Agency (ESA) (LanariCasu Manzo Zeni et al 2007 Michele et al 2008) Since then the technique hadbeen widely employed and confirmed to be a useful and powerful tool for monitoringsubtle surface deformation related to geodynamic phenomena over a long period oftime providing results with millimetre-level accuracy (Ferretti et al 2005 Gini ampLombardini 2005 Lanari Casu Manzo Zeni et al 2007 Herrera et al 2009 Tarikhi2010 Marghany 2011) In principle InSAR relies on the ability to measure differencein phase of two or more images to extract information about topography temporal sta-bility and deformation velocity (Kenyi amp Kaufmann 2003 Kampes amp Hanssen 2004Bamler et al 2005 Lee et al 2005 Lu amp Liao 2008) For topographical informationextraction digital elevation model (DEM) can be generated from two SAR imagestaken at the same position or slightly different positions with different view angles (spa-tial baseline) whereas deformation monitoring requires SAR images taken at differenttime intervals called temporal baseline (Canisius et al 2003 Bamler et al 2005Wegmuumlller et al 2009 Tarikhi 2010)

The investigation of the spatial and temporal pattern of ground deformation byanalysing a single interferogram that is derived from a pair of SAR images with the addi-tion of a DEM is called differential interferometry SAR (DInSAR) (Crosetto amp Crippa2005) Figure 2 illustrates the schematic concept of DInSAR for deformation measure-ment The DEM can be generated from global positioning system measurement digitaltopographic data or from interferometry derived from image pairs with short temporalbaseline and similar imaging geometry (Arjona Santoyo et al 2010 Tarikhi 2010)

Standard DInSAR configuration is flexible in providing qualitative information onthe deformation from two interferograms or a single interferogram and DEM Howeveratmospheric disturbance temporal decorrelation and inaccuracies of the external DEMconstitute major drawbacks that limit its operational capability (Cascini et al 2010Allen et al 2013) The foregoing limitations the need to quantify deformationphenomena in time series and the potentials to use available stacks of SAR images

Figure 1 DInSAR data collection schemeSource Tarikhi (2010)

2 OIdreesOI Mohammed et al

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collected from different sensors lead to the development of several advanced DInSARalgorithms (Michele et al 2008 Bhattacharya et al 2012 Calograve 2012) For the past13 years since the first differential interferometric SAR technique was developed (Fer-retti et al 2000 2005 Rosen et al 2000) researchers in the field of remote sensinghave been exploring various techniques to optimally measure deformation parametersfrom SAR data

The international literature is overwhelmed with reports of the successful applica-tions of advanced DInSAR algorithms to deformation monitoring However it is nolonger sufficient to lay much emphasis on the relatively high accuracy (millimetre-level)obtainable without a valid assessment of the challenges faced by researchers and usersof the products of A-DInSAR techniques Such challenges include the need to knowwhat particular deformation phenomenon a specific algorithm can suitably be appliedto Most of the literature on DInSAR for deformation studies revealed that the tech-niques perform well in urban and semi-urban and poorly in vegetated areas glacierand water bodies Also some of the papers discuss on various efforts made to improvethe performance (Kampes amp Hanssen 2004 Werner et al 2005 Cascini et al 2010Fan et al 2010 Tarikhi 2011 2012) Apart from this not much work had been reportedon the comparative analysis of the strengths and limitations of the techniques includingvalidation methods of interpretation and confident use of the result

In this study we reviewed the state-of-the-art of the established DInSAR techniquesfor deformation studies and current developments To do this we conducted a meta-analysis of literature to (i) investigate the deformation phenomenon that had been

Phase Denoising

Coregistration

SAR Simulation

Topographic PhaseSimulation

Differential Interferogram Generation

External DEM

Average IntensityGeneration

Oversample

Master Image

Coregistration

Slave Image

Interferogram Generation

Coherent Map Generation

Phase Unwrapping

Phase to Displacement Conversion

Geocoding

Figure 2 Schematic concept of DInSAR for deformation measurementSource Ng (2010)

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reported understanding what deformation phenomenon each is suitable for and whypoor performance in some areas (ii) expand knowledge on comparative analysis of thestrengths and weaknesses of each technique and (iii) stimulate readers to further theprogression of the diagnostic techniques for validating and interpreting DInSAR resultsOur focus in this review is limited to space-borne SAR sensors therefore airborneSAR platform and their applications will not be discussed The rest of this paper isorganised as follows Section 2 gives a brief description of persistent scatterer interfer-ometry (PSI) explaining the basic fundamentals and the criteria for identifying coherentpoints In Section 3 we have reviewed prominent A-DInSAR techniques with highlightof the basic features peculiar to each of those techniques Sources of data and the avail-able satellite sensors for SAR data collection are explicitly tabulated in Section 4 Dis-cussion of our findings is the focus of Section 5 while Section 6 gives the review ofcurrent issue Finally Section 7 draws the closing remark with the conclusion

2 Persistent Scatterer Interferometry

Persistent scatterers (PS) are pixels that remain consistent for years in a series of SARimages collected over an area with the same sensor (Ferretti et al 2000 2005 Caroet al 2011) PSI relies on identifying point targets which remain coherent in order toselect networks of points against which precise deformation velocity measurement canbe made Point targets are imaged cells exhibiting dominant scattering by a targetusually smaller in size than the resolution cell Figure 3 shows retrieved scatterssuperimposed on aerial photographs indicating urban features that exhibit persistentpoint targets They do not show speckle characteristics common to distributed targets(Wang et al 2008) The advantage of PS technique is that it forestalls the problem of

Figure 3 Permanent scatterers superimposed on aerial photograph of New OrleansSource httplabscasusfedugeodesysarhtml

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collected from different sensors lead to the development of several advanced DInSARalgorithms (Michele et al 2008 Bhattacharya et al 2012 Calograve 2012) For the past13 years since the first differential interferometric SAR technique was developed (Fer-retti et al 2000 2005 Rosen et al 2000) researchers in the field of remote sensinghave been exploring various techniques to optimally measure deformation parametersfrom SAR data

The international literature is overwhelmed with reports of the successful applica-tions of advanced DInSAR algorithms to deformation monitoring However it is nolonger sufficient to lay much emphasis on the relatively high accuracy (millimetre-level)obtainable without a valid assessment of the challenges faced by researchers and usersof the products of A-DInSAR techniques Such challenges include the need to knowwhat particular deformation phenomenon a specific algorithm can suitably be appliedto Most of the literature on DInSAR for deformation studies revealed that the tech-niques perform well in urban and semi-urban and poorly in vegetated areas glacierand water bodies Also some of the papers discuss on various efforts made to improvethe performance (Kampes amp Hanssen 2004 Werner et al 2005 Cascini et al 2010Fan et al 2010 Tarikhi 2011 2012) Apart from this not much work had been reportedon the comparative analysis of the strengths and limitations of the techniques includingvalidation methods of interpretation and confident use of the result

In this study we reviewed the state-of-the-art of the established DInSAR techniquesfor deformation studies and current developments To do this we conducted a meta-analysis of literature to (i) investigate the deformation phenomenon that had been

Phase Denoising

Coregistration

SAR Simulation

Topographic PhaseSimulation

Differential Interferogram Generation

External DEM

Average IntensityGeneration

Oversample

Master Image

Coregistration

Slave Image

Interferogram Generation

Coherent Map Generation

Phase Unwrapping

Phase to Displacement Conversion

Geocoding

Figure 2 Schematic concept of DInSAR for deformation measurementSource Ng (2010)

Geocarto International 3

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cart

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reported understanding what deformation phenomenon each is suitable for and whypoor performance in some areas (ii) expand knowledge on comparative analysis of thestrengths and weaknesses of each technique and (iii) stimulate readers to further theprogression of the diagnostic techniques for validating and interpreting DInSAR resultsOur focus in this review is limited to space-borne SAR sensors therefore airborneSAR platform and their applications will not be discussed The rest of this paper isorganised as follows Section 2 gives a brief description of persistent scatterer interfer-ometry (PSI) explaining the basic fundamentals and the criteria for identifying coherentpoints In Section 3 we have reviewed prominent A-DInSAR techniques with highlightof the basic features peculiar to each of those techniques Sources of data and the avail-able satellite sensors for SAR data collection are explicitly tabulated in Section 4 Dis-cussion of our findings is the focus of Section 5 while Section 6 gives the review ofcurrent issue Finally Section 7 draws the closing remark with the conclusion

2 Persistent Scatterer Interferometry

Persistent scatterers (PS) are pixels that remain consistent for years in a series of SARimages collected over an area with the same sensor (Ferretti et al 2000 2005 Caroet al 2011) PSI relies on identifying point targets which remain coherent in order toselect networks of points against which precise deformation velocity measurement canbe made Point targets are imaged cells exhibiting dominant scattering by a targetusually smaller in size than the resolution cell Figure 3 shows retrieved scatterssuperimposed on aerial photographs indicating urban features that exhibit persistentpoint targets They do not show speckle characteristics common to distributed targets(Wang et al 2008) The advantage of PS technique is that it forestalls the problem of

Figure 3 Permanent scatterers superimposed on aerial photograph of New OrleansSource httplabscasusfedugeodesysarhtml

4 OIdreesOI Mohammed et al

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reported understanding what deformation phenomenon each is suitable for and whypoor performance in some areas (ii) expand knowledge on comparative analysis of thestrengths and weaknesses of each technique and (iii) stimulate readers to further theprogression of the diagnostic techniques for validating and interpreting DInSAR resultsOur focus in this review is limited to space-borne SAR sensors therefore airborneSAR platform and their applications will not be discussed The rest of this paper isorganised as follows Section 2 gives a brief description of persistent scatterer interfer-ometry (PSI) explaining the basic fundamentals and the criteria for identifying coherentpoints In Section 3 we have reviewed prominent A-DInSAR techniques with highlightof the basic features peculiar to each of those techniques Sources of data and the avail-able satellite sensors for SAR data collection are explicitly tabulated in Section 4 Dis-cussion of our findings is the focus of Section 5 while Section 6 gives the review ofcurrent issue Finally Section 7 draws the closing remark with the conclusion

2 Persistent Scatterer Interferometry

Persistent scatterers (PS) are pixels that remain consistent for years in a series of SARimages collected over an area with the same sensor (Ferretti et al 2000 2005 Caroet al 2011) PSI relies on identifying point targets which remain coherent in order toselect networks of points against which precise deformation velocity measurement canbe made Point targets are imaged cells exhibiting dominant scattering by a targetusually smaller in size than the resolution cell Figure 3 shows retrieved scatterssuperimposed on aerial photographs indicating urban features that exhibit persistentpoint targets They do not show speckle characteristics common to distributed targets(Wang et al 2008) The advantage of PS technique is that it forestalls the problem of

Figure 3 Permanent scatterers superimposed on aerial photograph of New OrleansSource httplabscasusfedugeodesysarhtml

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temporal and geometrical decorrelation associated with the observed phase by selectingonly point-like scatterers The basic assumption of point targets is that point scattererare all correlated in ground range and height across the entire SAR data used in thestudy area Therefore they are expected to have an identical strength when processingimages of different looks PS processing analyses the phase of isolated coherent pointswith respect to time and space to estimate deformation value rather than the phase inspatial domain (Lu amp Liao 2008 Cuenca et al 2011)

21 Criterion for identifying point targets

Coherent point targets in SAR images can be identified using point-based or coherence-based criterion (Blanco-Sanchez et al 2008 Wang et al 2008) The former selects tar-gets with long-term stable backscattering behaviour Those targets generally do notexhibit speckle characteristics associated with distributed targets Moreover the methodenables point targets with the criterion of lower temporal amplitude variability (meansigma ratio) from a large number of SAR images to be identified The criterion pre-serves the full resolution of the image hence most PSI techniques employ coherent tar-get selection criterion In comparison the latter employs the mean spatial coherence asa measure where those pixels with values above a specified threshold are selected Thedrawbacks of these methods are reduction in spatial resolution and masking out of somepockets of isolated points More information can be found in Crosetto and Crippa(2005) and Wang et al (2008)

3 Advanced DInSAR techniques

The capabilities of PSI to use a large number of SAR images to reduce the effects ofatmospheric noise and to obtain highly precise deformation estimates spark off thedevelopment of several related algorithms All established advanced DInSAR algorithmsfor deformation monitoring are documented in the literature since 1999 and can begrouped into six distinct categories according to their patent nomenclature The first fiveuse point-based coherent target identification to select network of points to determinethe degree of deformation while the last one relies on coherence-based criterionHowever the order in which the algorithms are presented does not imply any rankingor any qualitative judgement Moreover because the algorithms are not entirelyindependent of the data sources and the deformation types for which their implementa-tion had been documented we examined their core principles and applications andtheir strength and limitation

31 Permanent scatterer interferometry SAR

Permanent scatterer interferometry is an improvement from conventional InSAR thatrelies on studying pixels which remains coherent over a sequence of interferogramsPSI algorithm uses amplitude criterion which estimates the phase standard deviationfrom each pixel from its temporal amplitude stability (Blanco-Sanchez et al 2008) Theobjective of this technique is to find quality point-like targets (permanent scatterer)instead of finding stable distributed targets as in the case of Coherent pixel technique(CPT) Figure 4 describes the basics of PS technique Researchers at Politecnico diMilano (Italy) developed PSI algorithm in 1999 as a new multi-image approach in

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which one searches the stack of images for objects on the ground providing consistentand stable radar reflections back to the satellite The technique was patented in 1999and licenced to Tele-Rilevamento Europa in 2000 to commercialize the technology(wwwtreuropacom) Details of the principles and applications can be found in Ferrettiet al (2000 2005) Kenyi and Kaufmann (2003) Kampes and Hanssen (2004) Meisinaet al (2006) Cuenca et al (2011) and Tarikhi (2011)

32 Interferometry point target analysis

Coherent point target analysis interferometry is a point-based method of identifying pointtargets with long-term stable backscattering characteristics The algorithm improvescoherent point identification based on jointly using stable spectral characteristics andlower intensity variability of the pixels to reliably select more coherent points from sin-gle or fewer sets of SAR Even if separated by large baselines errors resulting fromatmospheric artefacts are reduced and a higher accuracy can be achieved It is possibleto estimate the progressive terrain deformation with millimetric accuracy in urban areaswith many man-made features or terrain with exposed rock or outside cities where singleinfrastructures can be identified (Calograve 2012) Detail information about the principles andapplications can be found in Werner et al (2003 (2005) and Furuya et al (2007)

33 Small baseline subset

Small baseline subset is an advanced DInSAR technique with the capability to generateinterferograms from SAR data-sets to reduce both spatial and temporal decorrelationSmall baseline subset (SBAS) exploits two DInSAR images with small spatial and

Figure 4 Basics of the PS techniqueSource wwwtreuropacom

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which one searches the stack of images for objects on the ground providing consistentand stable radar reflections back to the satellite The technique was patented in 1999and licenced to Tele-Rilevamento Europa in 2000 to commercialize the technology(wwwtreuropacom) Details of the principles and applications can be found in Ferrettiet al (2000 2005) Kenyi and Kaufmann (2003) Kampes and Hanssen (2004) Meisinaet al (2006) Cuenca et al (2011) and Tarikhi (2011)

32 Interferometry point target analysis

Coherent point target analysis interferometry is a point-based method of identifying pointtargets with long-term stable backscattering characteristics The algorithm improvescoherent point identification based on jointly using stable spectral characteristics andlower intensity variability of the pixels to reliably select more coherent points from sin-gle or fewer sets of SAR Even if separated by large baselines errors resulting fromatmospheric artefacts are reduced and a higher accuracy can be achieved It is possibleto estimate the progressive terrain deformation with millimetric accuracy in urban areaswith many man-made features or terrain with exposed rock or outside cities where singleinfrastructures can be identified (Calograve 2012) Detail information about the principles andapplications can be found in Werner et al (2003 (2005) and Furuya et al (2007)

33 Small baseline subset

Small baseline subset is an advanced DInSAR technique with the capability to generateinterferograms from SAR data-sets to reduce both spatial and temporal decorrelationSmall baseline subset (SBAS) exploits two DInSAR images with small spatial and

Figure 4 Basics of the PS techniqueSource wwwtreuropacom

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temporal baseline between satellite orbits so as to optimise reliable coherent pointselection producing spatially dense long-term deformation maps (Qi-huan amp Xiu-feng2008) In addition the SBAS analysis is capable of producing deformation maps at bothlow and full spatial resolution respectively referred to as local and global scales AgainSBAS algorithm enables jointly processing multi-sensor SAR data acquired by differentradar systems with the same illumination geometry (ERS-12 and ENVISAT) It has theadvantage of deriving very long-term deformation time series from the vast availableSAR data collection Basic theory of the SBAS algorithm and applications can be seenin Lauknes et al (2005) Casu et al (2006ab) Lanari Casu Manzo Lundgren et al(2007) Lanari Casu Manzo Zeni et al (2007) Qi-huan and Xiu-feng (2008) Casciniet al (2010) and Canova et al (2012)

34 Stable point network

The stable point network (SPN) is an advanced DInSAR technique developed byALTAMIRA INFORMATION (Michele et al 2008 Calograve 2012) which exploits largesets of SAR images (12ndash25) over the same region to determine more accurate deforma-tion modelling capabilities and quality of the deformation estimation with high precision(Michele et al 2008) SPN analyses pixels from permanent features (such as buildingsbridges and rock) that maintain stable electromagnetic behaviour during the observationperiods These pixels are not affected by temporal decorrelation Atmospheric effectsare estimated and compensated in the analysis This allows for highly accurate displace-ment value for each stable point to be visually presented in the displacement evolutiontime-series chart More can be found in Herrera et al (2009)

35 Spatio-temporal unwrapping network

Spatio-temporal unwrapping network (STUN) is an advanced DInSAR algorithm basedon three dimensional (1D parametric temporal displacement model and 2D spatialunwrapping) in a single-master stack for optimal estimation of displacement parametersDetailed descriptions of the algorithm can be found in Kampes amp Adam (2006) STUNprocessing analysis models displacement for each stable scatterer using a linear combi-nation of base functions (functional model and stochastic model) the coefficient ofwhich are estimated simultaneously (Bamler et al 2005) with topographic averageatmospheric delay and the sub-pixel position terms Three basic features distinguishSTUN from other PSI-based A-DInSAR techniques (i) integer least-square estimatorfor resolving phase ambiguity with the highest probability and other key parametersincluding DEM error and subsidence velocity (ii) variance component random modelto weight the observations (noise and atmospheric artefacts) and (iii) alternativehypothesis tests to identify incorrect estimation and to ensure a consistent networkMore details about STUN can be seen in Kampes and Adam (2005) and Lu and Liao(2008)

36 Coherent pixel technique

CPT unlike the other PS is an advanced DInSAR algorithm based on coherencestability selection criteria of the pixels to be processed The algorithm was developed atthe Remote Sensing Laboratory (RSLab) Universitat Politegravecnica de Catalunya (UPC)Spain (Blanco-Sanchez et al 2008) The method extracts both linear and non-linear

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movement of the pixel DEM error for each selected pixel and atmospheric artefacts ofeach image (Domiacutenguez et al 2005 Blanco-Sanchez et al 2008 Arjona Monells et al2010) The processing Scheme requires generating best interferogram sets from amongavailable images employing mean spatial coherence as a measure where those pixelswith a coherence value above a given threshold in the whole stacks of images areselected and phase analysis is performed to calculate the mean velocity and deforma-tion time series within the observation period (Blanco-Sanchez et al 2008) Figure 5presents the processing steps for extracting linear and non-linear deformation estimatesWith CPT useful information can be obtained with a small stack of interferogramMoreover establishing a master image phase unwrapping of the noisy interferogramand DEM generation are not necessary hence the errors arising from atmosphericartefact and residual topography on the estimation of deformation are reduced Thedegree of precision of the estimated deformation is dependent on the coherent stabilitymaximum temporal and perpendicular baseline restriction and the permitted Dopplerdifference This notwithstanding CPT is reported to be suitable for surface deformationmeasurement over a wide spatial coverage and a span of time with millimetre precisionof deformation value More detail information can be seen in Romero et al (2005)Blanco-Sanchez et al (2007) Fernaacutendez et al (2009) and Arjona Monells et al(2010) and Arjona Santoyo et al (2010)

Figure 5 Block diagram of the linear (PRISAR) and non-linear (SUBSOFT) processing stagesof CPTSource Arjona Santoyo et al (2010)

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Other new algorithms for differential InSAR include corner reflector interferometrySAR (CRInSAR) (Fan et al 2010) along track interferometry (ATI) (Seigmund et al2004 Bamler et al 2005) liqui-InSAR (Tarikhi 2012) squeeSAR (Tarikhi 2011) andlastly fuzzy B-spline for coastal geomorphology reconstruction (Marghany 2011)

4 SAR data sources and processing software

According to Tarikhi (2010) satellite-based InSAR technique emerged in the 1980susing SEASAT data Incidentally the potentials of the technique received greater atten-tion in the 1990s following the launch of ERS-1 by ESA (1991) JERS-1 (1992)Radarsat-1 ERS-2 (1995) Table 1 shows the list of space-borne satellite sources forinterferometry SAR data

Similarly the possibility to use stacks of SAR data for deformation time-seriesestimation was first demonstrated in 1999 (Ferretti et al 2005) This developmentgenerated research interest that evolved the A-DInSAR algorithms earlier discussed inSection 3 Also a number of state-of-the-art processing softwares to exploit the largearchive of available SAR data especially ERS-12 were developed Table 2 presentsthe list of SAR interferometry processing software reported in the literature

5 Discussion

This study reveals that a number of surface deformations resulting from geodynamicphenomena can be mapped with A-DInSAR algorithms Though most methods providegenerally positive results few studies have compared and evaluated alternative methodsLu and Liao (2008) and Lauknes et al (2005) compared PInSARSTUN and SBASPInSAR for subsidence while Herrera et al (2009) compared SPNCPT for slow subsi-dence These works are limited to comparison of two techniques and therefore doesnot allow a critical evaluation across the domain

The only major research initiative testing the performance of more numbers ofA-DInSAR algorithms was undertaken under the lsquoDoris Projectrsquo initiated by TheEuropean Downstream Service For landslides and Subsidence Risk Management (Calograve2012) Under the project SBAS permanent scatterer interferometry SAR (PSInSAR)SPN and Interferometry point target analysis (IPTA) were reported to be used across

Table 1 Satellite-based SAR sensors and their characteristics

Sensor Band Year Resolution Swath Operator

SeaSat S 1978 25m 100 km NASAERS-1-2 C 1991 1995 30m 100 km ESASRTM 2000 NASARADARSAT 12 C 1995 2007 3ndash100m 20ndash500 km CanadaASAR-ENVISAT C 2002 30m 56ndash100 km ESAJERS C amp L (FP) 1992 75 km JAXAALOS-PALSAR L (F D P) 2006 10ndash100m 30ndash550 km JAXATanDEM-X X 2010 750 km GermanyTerraSAR-X X 2007 1ndash16m 10ndash150 km Infoterra GmbH

GermanyCosmo-SkyMed X (FP) 2007 1ndash100m 7ndash200 km eGeos ItalyRISAT-1 C 2012 3ndash50m 30ndash240 km ISRO India

Geocarto International 9

Geo

cart

o In

tern

atio

nal

Table

2Interferom

etry

SARprocessing

software

Softwarename

Develop

erproprietor

Linklicence

type

Capability

DIA

PASON

CNES

wwwaltamira-inform

ationcom

commercial

licence

byAltamira

Inform

ation

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

DORIS

TU

Delft

wwwenterpriselrtu

delftn

ldoris

free

licence

forno

n-commercial

purposes

MostsuitedforERSENVISATbu

tim

plem

entedon

JERSRADARSAT-1

ALOSandTSX

EarthView-InS

AR

MDA

GSI

httpgsm

dacorporationcom

SatelliteD

ataRadarsat2OpenE

vaspx

ThispartEarthView

OpenE

VisFree

Adv

ancedSAR

ProcessorERS-1ERS-2RS-

TandemJERS-1RADARSAT

Env

isat

andALOS

(JAXA)sensors

ENVI(SARscape)

ResearchSystemsInc

(RSI)

wwwrsinccom

env

icommercial

licence

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

GAMMA

Gam

ma

wwwgam

ma-rschcommercial

licence

Adv

ancedDInSAR

with

ERS12

JERS-1SIR-C

X-

SARRADARSAT

ENVISAT

Generic

SAR

Norut

ITwwwerdascom

Com

mercial

availablein

ERDAS

RADARSAT-1-2ERS-1-2

Env

isat

ASARALOS

PALSARTerraSAR-X

andCosmo-Sky

Med

IDIO

TCom

puterVisionamp

RSGroup

(Berlin

University

ofTechn

olog

y)Free

non-commercial

purpose

httpwwwcvtu-berlin

deidiot

Stand

ardDInSAR

with

justENVISAT-ASAR

IMAGIN

E-InS

AR

Leica

Geosystem

sGeospatialIm

aging

LLC

httpwwwamerisurvcomcon

tent

view

388

42

httpwwwim

agem

nlnode115Com

mercial

Adv

ancedinterferom

etricSAR

processing

PolSARpro

Und

erESA

contract

comprising

University

ofRennes1

The

Microwaves

andRadar

Institu

te(H

R)of

DLRAELamp

others

Availablefree

httpeartheoesain

tpo

lsarpro

Stand

ardDInSAR

with

mAirbo

rne

AIRSAR

ampTOPSAREMISARE-SARPi-SARRAMSES

Spacebo

rneSIR-CEnv

isat

ASARRADARSAT-2

ALOSPA

LSARTerraSAR-X

ROI-PA

CBerkley

University

wwwopenchann

elfoun

datio

norg

free

licence

forno

n-commercial

purposes

BuildingDEMRetrievingSARData

Creating

InSAR

image

SUBSOFT

RSLabUPC

Calculatin

gno

n-lin

earof

DInSAR

with

CPT

VEXCEL3D

SAR

GeoscienceAustralia

wwwvexcelcom

httpsw

ww

tendersgovauevent=publiccn

commercial

licence

RProcessing

Interferom

etric

DInSAR

ampOrtho

rectificatio

n

10 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

movement of the pixel DEM error for each selected pixel and atmospheric artefacts ofeach image (Domiacutenguez et al 2005 Blanco-Sanchez et al 2008 Arjona Monells et al2010) The processing Scheme requires generating best interferogram sets from amongavailable images employing mean spatial coherence as a measure where those pixelswith a coherence value above a given threshold in the whole stacks of images areselected and phase analysis is performed to calculate the mean velocity and deforma-tion time series within the observation period (Blanco-Sanchez et al 2008) Figure 5presents the processing steps for extracting linear and non-linear deformation estimatesWith CPT useful information can be obtained with a small stack of interferogramMoreover establishing a master image phase unwrapping of the noisy interferogramand DEM generation are not necessary hence the errors arising from atmosphericartefact and residual topography on the estimation of deformation are reduced Thedegree of precision of the estimated deformation is dependent on the coherent stabilitymaximum temporal and perpendicular baseline restriction and the permitted Dopplerdifference This notwithstanding CPT is reported to be suitable for surface deformationmeasurement over a wide spatial coverage and a span of time with millimetre precisionof deformation value More detail information can be seen in Romero et al (2005)Blanco-Sanchez et al (2007) Fernaacutendez et al (2009) and Arjona Monells et al(2010) and Arjona Santoyo et al (2010)

Figure 5 Block diagram of the linear (PRISAR) and non-linear (SUBSOFT) processing stagesof CPTSource Arjona Santoyo et al (2010)

8 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

Other new algorithms for differential InSAR include corner reflector interferometrySAR (CRInSAR) (Fan et al 2010) along track interferometry (ATI) (Seigmund et al2004 Bamler et al 2005) liqui-InSAR (Tarikhi 2012) squeeSAR (Tarikhi 2011) andlastly fuzzy B-spline for coastal geomorphology reconstruction (Marghany 2011)

4 SAR data sources and processing software

According to Tarikhi (2010) satellite-based InSAR technique emerged in the 1980susing SEASAT data Incidentally the potentials of the technique received greater atten-tion in the 1990s following the launch of ERS-1 by ESA (1991) JERS-1 (1992)Radarsat-1 ERS-2 (1995) Table 1 shows the list of space-borne satellite sources forinterferometry SAR data

Similarly the possibility to use stacks of SAR data for deformation time-seriesestimation was first demonstrated in 1999 (Ferretti et al 2005) This developmentgenerated research interest that evolved the A-DInSAR algorithms earlier discussed inSection 3 Also a number of state-of-the-art processing softwares to exploit the largearchive of available SAR data especially ERS-12 were developed Table 2 presentsthe list of SAR interferometry processing software reported in the literature

5 Discussion

This study reveals that a number of surface deformations resulting from geodynamicphenomena can be mapped with A-DInSAR algorithms Though most methods providegenerally positive results few studies have compared and evaluated alternative methodsLu and Liao (2008) and Lauknes et al (2005) compared PInSARSTUN and SBASPInSAR for subsidence while Herrera et al (2009) compared SPNCPT for slow subsi-dence These works are limited to comparison of two techniques and therefore doesnot allow a critical evaluation across the domain

The only major research initiative testing the performance of more numbers ofA-DInSAR algorithms was undertaken under the lsquoDoris Projectrsquo initiated by TheEuropean Downstream Service For landslides and Subsidence Risk Management (Calograve2012) Under the project SBAS permanent scatterer interferometry SAR (PSInSAR)SPN and Interferometry point target analysis (IPTA) were reported to be used across

Table 1 Satellite-based SAR sensors and their characteristics

Sensor Band Year Resolution Swath Operator

SeaSat S 1978 25m 100 km NASAERS-1-2 C 1991 1995 30m 100 km ESASRTM 2000 NASARADARSAT 12 C 1995 2007 3ndash100m 20ndash500 km CanadaASAR-ENVISAT C 2002 30m 56ndash100 km ESAJERS C amp L (FP) 1992 75 km JAXAALOS-PALSAR L (F D P) 2006 10ndash100m 30ndash550 km JAXATanDEM-X X 2010 750 km GermanyTerraSAR-X X 2007 1ndash16m 10ndash150 km Infoterra GmbH

GermanyCosmo-SkyMed X (FP) 2007 1ndash100m 7ndash200 km eGeos ItalyRISAT-1 C 2012 3ndash50m 30ndash240 km ISRO India

Geocarto International 9

Geo

cart

o In

tern

atio

nal

Table

2Interferom

etry

SARprocessing

software

Softwarename

Develop

erproprietor

Linklicence

type

Capability

DIA

PASON

CNES

wwwaltamira-inform

ationcom

commercial

licence

byAltamira

Inform

ation

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

DORIS

TU

Delft

wwwenterpriselrtu

delftn

ldoris

free

licence

forno

n-commercial

purposes

MostsuitedforERSENVISATbu

tim

plem

entedon

JERSRADARSAT-1

ALOSandTSX

EarthView-InS

AR

MDA

GSI

httpgsm

dacorporationcom

SatelliteD

ataRadarsat2OpenE

vaspx

ThispartEarthView

OpenE

VisFree

Adv

ancedSAR

ProcessorERS-1ERS-2RS-

TandemJERS-1RADARSAT

Env

isat

andALOS

(JAXA)sensors

ENVI(SARscape)

ResearchSystemsInc

(RSI)

wwwrsinccom

env

icommercial

licence

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

GAMMA

Gam

ma

wwwgam

ma-rschcommercial

licence

Adv

ancedDInSAR

with

ERS12

JERS-1SIR-C

X-

SARRADARSAT

ENVISAT

Generic

SAR

Norut

ITwwwerdascom

Com

mercial

availablein

ERDAS

RADARSAT-1-2ERS-1-2

Env

isat

ASARALOS

PALSARTerraSAR-X

andCosmo-Sky

Med

IDIO

TCom

puterVisionamp

RSGroup

(Berlin

University

ofTechn

olog

y)Free

non-commercial

purpose

httpwwwcvtu-berlin

deidiot

Stand

ardDInSAR

with

justENVISAT-ASAR

IMAGIN

E-InS

AR

Leica

Geosystem

sGeospatialIm

aging

LLC

httpwwwamerisurvcomcon

tent

view

388

42

httpwwwim

agem

nlnode115Com

mercial

Adv

ancedinterferom

etricSAR

processing

PolSARpro

Und

erESA

contract

comprising

University

ofRennes1

The

Microwaves

andRadar

Institu

te(H

R)of

DLRAELamp

others

Availablefree

httpeartheoesain

tpo

lsarpro

Stand

ardDInSAR

with

mAirbo

rne

AIRSAR

ampTOPSAREMISARE-SARPi-SARRAMSES

Spacebo

rneSIR-CEnv

isat

ASARRADARSAT-2

ALOSPA

LSARTerraSAR-X

ROI-PA

CBerkley

University

wwwopenchann

elfoun

datio

norg

free

licence

forno

n-commercial

purposes

BuildingDEMRetrievingSARData

Creating

InSAR

image

SUBSOFT

RSLabUPC

Calculatin

gno

n-lin

earof

DInSAR

with

CPT

VEXCEL3D

SAR

GeoscienceAustralia

wwwvexcelcom

httpsw

ww

tendersgovauevent=publiccn

commercial

licence

RProcessing

Interferom

etric

DInSAR

ampOrtho

rectificatio

n

10 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

Other new algorithms for differential InSAR include corner reflector interferometrySAR (CRInSAR) (Fan et al 2010) along track interferometry (ATI) (Seigmund et al2004 Bamler et al 2005) liqui-InSAR (Tarikhi 2012) squeeSAR (Tarikhi 2011) andlastly fuzzy B-spline for coastal geomorphology reconstruction (Marghany 2011)

4 SAR data sources and processing software

According to Tarikhi (2010) satellite-based InSAR technique emerged in the 1980susing SEASAT data Incidentally the potentials of the technique received greater atten-tion in the 1990s following the launch of ERS-1 by ESA (1991) JERS-1 (1992)Radarsat-1 ERS-2 (1995) Table 1 shows the list of space-borne satellite sources forinterferometry SAR data

Similarly the possibility to use stacks of SAR data for deformation time-seriesestimation was first demonstrated in 1999 (Ferretti et al 2005) This developmentgenerated research interest that evolved the A-DInSAR algorithms earlier discussed inSection 3 Also a number of state-of-the-art processing softwares to exploit the largearchive of available SAR data especially ERS-12 were developed Table 2 presentsthe list of SAR interferometry processing software reported in the literature

5 Discussion

This study reveals that a number of surface deformations resulting from geodynamicphenomena can be mapped with A-DInSAR algorithms Though most methods providegenerally positive results few studies have compared and evaluated alternative methodsLu and Liao (2008) and Lauknes et al (2005) compared PInSARSTUN and SBASPInSAR for subsidence while Herrera et al (2009) compared SPNCPT for slow subsi-dence These works are limited to comparison of two techniques and therefore doesnot allow a critical evaluation across the domain

The only major research initiative testing the performance of more numbers ofA-DInSAR algorithms was undertaken under the lsquoDoris Projectrsquo initiated by TheEuropean Downstream Service For landslides and Subsidence Risk Management (Calograve2012) Under the project SBAS permanent scatterer interferometry SAR (PSInSAR)SPN and Interferometry point target analysis (IPTA) were reported to be used across

Table 1 Satellite-based SAR sensors and their characteristics

Sensor Band Year Resolution Swath Operator

SeaSat S 1978 25m 100 km NASAERS-1-2 C 1991 1995 30m 100 km ESASRTM 2000 NASARADARSAT 12 C 1995 2007 3ndash100m 20ndash500 km CanadaASAR-ENVISAT C 2002 30m 56ndash100 km ESAJERS C amp L (FP) 1992 75 km JAXAALOS-PALSAR L (F D P) 2006 10ndash100m 30ndash550 km JAXATanDEM-X X 2010 750 km GermanyTerraSAR-X X 2007 1ndash16m 10ndash150 km Infoterra GmbH

GermanyCosmo-SkyMed X (FP) 2007 1ndash100m 7ndash200 km eGeos ItalyRISAT-1 C 2012 3ndash50m 30ndash240 km ISRO India

Geocarto International 9

Geo

cart

o In

tern

atio

nal

Table

2Interferom

etry

SARprocessing

software

Softwarename

Develop

erproprietor

Linklicence

type

Capability

DIA

PASON

CNES

wwwaltamira-inform

ationcom

commercial

licence

byAltamira

Inform

ation

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

DORIS

TU

Delft

wwwenterpriselrtu

delftn

ldoris

free

licence

forno

n-commercial

purposes

MostsuitedforERSENVISATbu

tim

plem

entedon

JERSRADARSAT-1

ALOSandTSX

EarthView-InS

AR

MDA

GSI

httpgsm

dacorporationcom

SatelliteD

ataRadarsat2OpenE

vaspx

ThispartEarthView

OpenE

VisFree

Adv

ancedSAR

ProcessorERS-1ERS-2RS-

TandemJERS-1RADARSAT

Env

isat

andALOS

(JAXA)sensors

ENVI(SARscape)

ResearchSystemsInc

(RSI)

wwwrsinccom

env

icommercial

licence

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

GAMMA

Gam

ma

wwwgam

ma-rschcommercial

licence

Adv

ancedDInSAR

with

ERS12

JERS-1SIR-C

X-

SARRADARSAT

ENVISAT

Generic

SAR

Norut

ITwwwerdascom

Com

mercial

availablein

ERDAS

RADARSAT-1-2ERS-1-2

Env

isat

ASARALOS

PALSARTerraSAR-X

andCosmo-Sky

Med

IDIO

TCom

puterVisionamp

RSGroup

(Berlin

University

ofTechn

olog

y)Free

non-commercial

purpose

httpwwwcvtu-berlin

deidiot

Stand

ardDInSAR

with

justENVISAT-ASAR

IMAGIN

E-InS

AR

Leica

Geosystem

sGeospatialIm

aging

LLC

httpwwwamerisurvcomcon

tent

view

388

42

httpwwwim

agem

nlnode115Com

mercial

Adv

ancedinterferom

etricSAR

processing

PolSARpro

Und

erESA

contract

comprising

University

ofRennes1

The

Microwaves

andRadar

Institu

te(H

R)of

DLRAELamp

others

Availablefree

httpeartheoesain

tpo

lsarpro

Stand

ardDInSAR

with

mAirbo

rne

AIRSAR

ampTOPSAREMISARE-SARPi-SARRAMSES

Spacebo

rneSIR-CEnv

isat

ASARRADARSAT-2

ALOSPA

LSARTerraSAR-X

ROI-PA

CBerkley

University

wwwopenchann

elfoun

datio

norg

free

licence

forno

n-commercial

purposes

BuildingDEMRetrievingSARData

Creating

InSAR

image

SUBSOFT

RSLabUPC

Calculatin

gno

n-lin

earof

DInSAR

with

CPT

VEXCEL3D

SAR

GeoscienceAustralia

wwwvexcelcom

httpsw

ww

tendersgovauevent=publiccn

commercial

licence

RProcessing

Interferom

etric

DInSAR

ampOrtho

rectificatio

n

10 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

Table

2Interferom

etry

SARprocessing

software

Softwarename

Develop

erproprietor

Linklicence

type

Capability

DIA

PASON

CNES

wwwaltamira-inform

ationcom

commercial

licence

byAltamira

Inform

ation

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

DORIS

TU

Delft

wwwenterpriselrtu

delftn

ldoris

free

licence

forno

n-commercial

purposes

MostsuitedforERSENVISATbu

tim

plem

entedon

JERSRADARSAT-1

ALOSandTSX

EarthView-InS

AR

MDA

GSI

httpgsm

dacorporationcom

SatelliteD

ataRadarsat2OpenE

vaspx

ThispartEarthView

OpenE

VisFree

Adv

ancedSAR

ProcessorERS-1ERS-2RS-

TandemJERS-1RADARSAT

Env

isat

andALOS

(JAXA)sensors

ENVI(SARscape)

ResearchSystemsInc

(RSI)

wwwrsinccom

env

icommercial

licence

Stand

ardDInSAR

with

ERS12

JERS-1

RADARSAT

ENVISAT

GAMMA

Gam

ma

wwwgam

ma-rschcommercial

licence

Adv

ancedDInSAR

with

ERS12

JERS-1SIR-C

X-

SARRADARSAT

ENVISAT

Generic

SAR

Norut

ITwwwerdascom

Com

mercial

availablein

ERDAS

RADARSAT-1-2ERS-1-2

Env

isat

ASARALOS

PALSARTerraSAR-X

andCosmo-Sky

Med

IDIO

TCom

puterVisionamp

RSGroup

(Berlin

University

ofTechn

olog

y)Free

non-commercial

purpose

httpwwwcvtu-berlin

deidiot

Stand

ardDInSAR

with

justENVISAT-ASAR

IMAGIN

E-InS

AR

Leica

Geosystem

sGeospatialIm

aging

LLC

httpwwwamerisurvcomcon

tent

view

388

42

httpwwwim

agem

nlnode115Com

mercial

Adv

ancedinterferom

etricSAR

processing

PolSARpro

Und

erESA

contract

comprising

University

ofRennes1

The

Microwaves

andRadar

Institu

te(H

R)of

DLRAELamp

others

Availablefree

httpeartheoesain

tpo

lsarpro

Stand

ardDInSAR

with

mAirbo

rne

AIRSAR

ampTOPSAREMISARE-SARPi-SARRAMSES

Spacebo

rneSIR-CEnv

isat

ASARRADARSAT-2

ALOSPA

LSARTerraSAR-X

ROI-PA

CBerkley

University

wwwopenchann

elfoun

datio

norg

free

licence

forno

n-commercial

purposes

BuildingDEMRetrievingSARData

Creating

InSAR

image

SUBSOFT

RSLabUPC

Calculatin

gno

n-lin

earof

DInSAR

with

CPT

VEXCEL3D

SAR

GeoscienceAustralia

wwwvexcelcom

httpsw

ww

tendersgovauevent=publiccn

commercial

licence

RProcessing

Interferom

etric

DInSAR

ampOrtho

rectificatio

n

10 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

some European countries to investigate the spatial and temporal pattern of grounddeformation induced by landslides and subsidence phenomena However no compara-tive analysis is given on the performance of the techniques In the authorsrsquo opinionperformance analysis may either be outside the research scope (which is as quotedbelow) or because the project is still ongoing The authors have asserted that theresearch scope is lsquoto improving the understanding of the complex phenomena thatcause ground deformation and at fostering the current capabilities of Civil Defenceauthorities at different administrative and operation levels to manage the associatedrisksrsquo (Calograve 2012)

Generally findings reveal that all the A-DInSAR algorithms have the capability toevaluate the deformation evolution in time series at millimetre accuracy levelMeanwhile this level of accuracy is limited to urban areas In rural and vegetated areasthey perform poorly due to insufficient permanent scatterers on which accurate deforma-tion measurements rely The study shows that SPN and STUN were used solely forsubsidence detection PSInSAR records major success in subsidence measurement withfew applications on landslide and glacier studies Similarly CPT appears to favourslowly moving subsidence (or displacement) with capability to monitor structuraldeformation like dams as which are non-linear in nature IPTA on the other handprovides reliable results in seismic tectonic uplift and mining subsidence measure-ments The SBAS proposed by Mora (Mora et al 2003 Crosetto amp Pasquali 2008) forlinear and non-linear displacement provides excellent results over a wide range ofdeformation types such as subsidence landslide tectonic and seismic fault creep andaquifer It is worth to mention that virtually all the algorithms are used for subsidencemeasurement in particular and they all give appreciable results except in vegetated andhilly areas This implies that the choice of a particular technique is not just a functionof the capabilities of the algorithm itself but a combination of other factors like thenumber of SAR data available the spatial extent deformation products the nature ofthe terrain deformation type and the processing software available

The findings of this paper also divulge the fact that temporal and geometrical decor-relation accounts for low coherence in vegetated areas In Table 1 it can be observedthat most SAR satellite sensors use the C-band of the microwave portion for datacollection However in suburban areas this region of microwave signal is affected byvegetation canopy in two ways One the mean height recorded will lie between theground surface and the top of the canopy and second volume scattering will causedecrease in correlation coefficient of the interferometry Besides the signal penetrationissue the local incidence angle which varies with slope equally affects the level ofcoherence

Furthermore aliasing caused by fast moving object also limits the application of A-DInSAR for glacier monitoring This accounts for the keen interest in algorithm andsensor development (discussed later in this Section) so as to increase the amount ofcoherent points and as well expand the application domain to other surfaces such aswater body and seandashcoastal geomorphologic interaction Looking at the relatively shortperiod of the development of advanced DInSAR techniques (since 13 years ago) andthe train of research at different levels regions and for different deformation purposesit is not easy to rank the techniques from a technical point of view However thestrength and limitations of each technique are presented in Table 3

As DInSAR technique is growing in complexity so the issues of validation andinterpretation of the result become more challenging Traditionally validation oraccuracy assessment of derived data-sets from remote sensing data involves comparing

Geocarto International 11

Geo

cart

o In

tern

atio

nal

Table 3 Strength and limitations of A-DInSAR techniques

Technique Strength Limitations

PSInSAR bull Not affected by geometricalDoppler centroid nor temporaldecorrelation

bull No restriction regarding the maxi-mum spatial and temporal baselineand Doppler difference

bull Generation of a displacementtrend and changes thereto overtime

bull Measurement in millimetres ofthe lsquoverticalrsquo dimension beingmore accurate than that of GPS

bull Provides a spatial density of mea-surement points not achievablewith conventional techniques

bull Ability to monitor movementwithin discrete temporal subsetsof a full data set

bull Requires large numbers of imageand presence of sufficient numberof PS

bull Target motion must be slowenough to avoid aliasing

bull Precision depends on processingall images at once difficult to inferthe PS distribution in an area with-out significant data processing

bull Interferograms can only be gener-ated from SAR data acquired bythe same satellite

bull If prior information on groundmotion is unavailable phase-unwrapping problems (phase ali-asing) limit the maximum dis-placement between twoconsecutive acquisitions to lessthan 14mm

IPTA bull Use fewer numbers of SAR data

bull Use multiple master images

bull Combination of small baselineimages reduces the impacts ofDEM error and spatial decorrela-tion

bull Multi sensors processing

bull Performance of multiple-imageprocessing l requires individualinterferogram generation

bull Not suitable for non-linear defor-mation history retriever

bull Impossible to define a propagationerror function as a result of diffi-culty in phase unwrapping

SBAS bull Increases sampling rate and pro-vides spatially dense deformationmaps

bull Carry out analysis at low and fullspatial resolutions

bull Suitable for small-scale analysisover wide area

bull Jointly process multi-sensor SARdata collected by different radarsystems

bull Inversion of the unwrapped interf-erogarms using SDV methodallows merging information fromcomputed interferograms andreduces the effect of topographicartefacts present in the DEM usedto compute the differential inter-ferograms

bull Detect and remove the atmo-spheric artefacts and the orbitalramps using the available timespace information

bull Rely on SAR data with small spa-tial and temporal baselines

bull Highly sensitive to geometric dec-orrelation

bull Direct phase unwrapping repre-sents a potential source of errors

(Continued)

12 OIdreesOI Mohammed et al

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Table 3 (Continued)

Technique Strength Limitations

SPN bull Multiple-master image capable ofmeasuring wide area

bull Less sensitive to geometric decor-relation than the SBAS

bull Multi-sensor SAR data processing

bull Provides parameters for checkingthe quality of deformation esti-mates

bull A linear deformation model tounwrap the phases so need notdirectly unwrapping the differen-tial interferograms which is apotential source of errors

bull Precise geocoding of the DInSARproducts based on estimated resid-ual topographic errors

bull Displacement evolution can bevisualized from time-series chart

bull Efficient for urban semi-urbanand rural areas at great detail

bull Can estimate both linear and non-linear deformation components

bull Result depends on the distancefrom reference point

bull Product precision depends on theelectromagnetic stability of thePS the number of available SARimages and the quality of the lin-ear model

STUN bull Reduces number of incorrectlyestimated ambiguity

bull Automatic detection of incorrectlyprocessed interferograms

bull Identify PS with limited numberof observations

bull Stochastic model adjusts differentweight for interferometric datatherefore provides realistic qualityof estimates

bull Precision decreases the furtheraway from reference points due todifference in atmospheric signalwith distance

CPT bull Linear and non-linear deformationcan be extracted

bull Reduces DEM error and atmo-spheric artefacts

bull More reliable and robust whendealing with low number of inter-ferograms

bull The establishing master image isnot required

bull Does not require phase-unwrap-ping process

bull CPT can be carried out withoutDEM

bull Averaging of interferogramsreduces the spatial resolution

bull Isolated single-point targets aremasked out

bull Low coherence limits the numberof pixels that can be included inthe processing

Geocarto International 13

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them against independent or reference data source assumed to be correct Presentlyresearch effort on A-DInSAR techniques for deformation monitoring is being muchmore focused on algorithm and sensor development to enhance the data sampling thanvalidation Notwithstanding the research community is also concerned with the reliabil-ity of the measurements obtained from those algorithms The project initiated by ESAfollowing a recommendation made during the Fringe 2003 Workshop to assess the reli-ability and dependability of PSI methods code named PSIC4 (Persistent Scatterer Inter-ferometry Codes Cross-Comparison and Certification for long-term differentialinterferometry) is the only major research effort targeted at comparison and validation(Crosetto and Crippa 2005 Herrera et al 2009)

Few of the validation attempts basically compared DInSAR result with the result ofdifferent reference data such as levelling GPS observation extensometer and otherin situ data previously conducted One can see that as opposed to other remote sensingapplications like change detection where established statistical techniques for validatingclassifications are in place validating DInSAR derived data-set is yet to attain maturityBesides the work of Lu and Liao (2008) that reports validating DInSAR data with lev-elling using the nearest neighbouring method all others are based on non-rigorous mea-sure of central tendency and dispersion that is the mean and standard deviationAnother point is that inadequate ground truth data impede the efforts in providing com-prehensive scheme or framework for validation purposes

Variation in human interpretation can have a significant impact on what is consid-ered correct Although quite interesting results are reported in most cases it can beobserved that interpretation does not go beyond simple qualitative and quantitative anal-ysis without a comprehensive explanation of the underlying processes behind theresults This could be as a result of inadequate background knowledge about the causesof most of the deformation patterns observed or lack of established standards for inter-preting results within the research community Or again could be as a result of thecomplexity associated with interpreting some deformation phenomenon such as faultcreep

6 Current issues

In this Section we highlight the recent developments in interferometry SAR technologyOur discussion focuses on recent issues on satellite sensors and algorithm developmentSAR satellites for data acquisition are still few in number limiting the amount of dataavailable Aside the fewer satellite sensors for SAR data collection the early generationof sensors such as ERS-12 ASAR-ENVISAR and RADARSAT-12 employing the C-band of microwave portion is the most exploited SAR data sources for deformationmonitoring The low penetrability of C-band limits its success especially in vegetatedareas

61 Sensor development

TanDEM-X (2010) TeraSAR-X and Cosmo-SkyMed (2007) using the X-band andALOS-PALSAR (2006) exploiting the P-band are recent satellite missions employinglonger microwave signals These SAR sensors are expected to offer significant improve-ment in deformation measurement and also to widen the application domain of SARdata

14 OIdreesOI Mohammed et al

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Future SAR satellite missions include ALOS-2 PALSA-2 by JAXA employing L-band and SENTINEL-1 sensor by ESA employing C-band which are both planned tobe launched in 2013 (ESA 2012 JAXA 2012 Mouginot et al 2012)

Also in the area of satellite sensor design researchers are exploring the use of a sin-gle pass with different look angles for InSAR data collection Consequently this willreduce the temporal and spatial baseline to almost zero and make it more applicable forprecise monitoring of water body displacement and the direction of flow

62 Development in algorithm

As mentioned in Section 3 four new A-DInSAR algorithms have evolved in recenttimes in response to the complex nature of some deformation phenomena and the needfor obtaining better coherence in glacier and highly vegetated areas These new algo-rithms are CRInSAR ATI liqui-InSAR and squeeSAR CRInSAR is purposelydesigned as a powerful tool in vegetated areas It involves the installation of man-madereflector in study area to provide persistent scatterer over time series for deformationmonitoring ATI use for detecting and measuring moving objects with SAR techniquesis usually referred to as ground moving target indication It allows for a much betterdetection of moving objects than single-channel SARs hence its growing application intraffic monitoring

A novel achievement in A-DInSAR research for deformation monitoring isliqui-InSAR algorithm developed for monitoring aquatic bodies The technique allowsfor generating fringe patterns on the water bodies using SAR images with a temporalbaseline of maximum 16 s SqueeSAR is another new technique that squeezes theeffects of both persistent and distributed scatterers (DS) SqueeSAR enables the detec-tion of movement in areas dominated by DS It should be noted that these techniquesare still evolving and hence limited to research applications for refinement and evalua-tion of their performances Lastly in the list new development in the algorithm is theFuzzy B-Spline algorithm It was experimented with a group of researchers (Marghany2011) in Universiti Teknologi Malaysia for mapping 3D coastal geomorphology recon-struction The result indicates its potential for measuring ocean fringes and other aquaticgeomorphological characteristics

7 Conclusion

The capabilities of space-borne interferometry SAR data have generated greatenthusiasm over the prospect of establishing remote sensing-based system forcontinuous surface deformation monitoring Today it is well established that a numberof algorithms can be used to estimate deformation time series on land surface and otherbodies from SAR images The result of this study shows that more robust methods andsatellite sensor development design are receiving the attention of researchers to furtherreduce the inherent limitations of the technology

Although all of the A-DInSAR techniques had been applied to different deformationphenomena using data sources from different sensors considerations from the reviewlead to the following conclusions

(1) The suitability of a technique for surface deformation depends on many factorssuch as the number of SAR data available nature and extent of the terraindeformation result expected and the nature of the deformation under study

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(2) Poor performance in some areas is due to low coherence caused by the interac-tion of the radar signal to the surface (like vegetation glacier) the speed of theobject under study and temporal and spatial decorrelations

(3) Improvement in algorithm and sensors to reduce spatial and temporal baseline isbringing the techniques into a state of convergence for enhancement

(4) Again point 3 above and the capability to merge data from different sensors intoone processing module (as in the case of SBAS CPT and SPN) make themmore versatile

(5) More research is needed to establish rigorous statistically founded methods ofvalidation of the result

(6) Availability of more SAR sensors collecting data at longer wavelength will pro-vide the research community with more data for processing and better results

ReferencesAllen TR Wanga Y Gore B 2013 Coastal wetland mapping combining multi-date SAR and

LiDAR Geocarto Int [16 pp advance online publication] doi101080101060492013768297

Arjona A Monells D Fernandez J Duque S Mallorqui J 2010 Deformation analysis employingthe coherent pixel technique and ENVISAT and ERS images in Canary Islands Fringe 2009Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol 2009

Arjona A Santoyo MA Fernaacutendez J Monells D Prieto JF Pallero JLG Prieto E Seco A LuzoacutenF Mallorquiacute J 2010 On the applicability of an advanced dinsar technique near Itoiz and YesaReservoirs Navarra Spain Fringe 2009 Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol2009 p 2ndash7

Bamler R Kampes B Adam N Suchandt S 2005 Assessment of slow deformations and rapidmotions by radar interferometry In Fritsch D editor Photogrammetric week 05 HeidelbergWichmann Verlag p 1ndash12

Bhattacharya A Arora MK Sharma ML 2012 Usefulness of synthetic aperture radar (SAR)interferometry for digital elevation model (DEM) generation and estimation of land surfacedisplacement in Jharia coal field area Geocarto Int 2737ndash41

Blanco-Sanchez P Duque S Mallorqui JJ Monells D 2007 Analysis of highly non-linear defor-mations due to mining activity with dinsar PSIC4 test site Envisat Symposium 2007 2007April 23ndash27 Montreux Switzerland

Blanco-Sanchez P Mallorqui JJ Duque S Monells D 2008 The coherent pixels technique(CPT) an advanced dinsar technique for nonlinear deformation monitoring Pure Appl Geo-phys 1651167ndash1193

Calograve F 2012 Doris project the european downstream service forlandslides and subsidence riskmanagement Geoscience and Remote Sensing Symposium (IGARSS) 2012 IEEE Interna-tional 2012 July 22ndash27 Naples Italy p 3018ndash3021

Canisius J Honda K Tokunaga M 2003 Detection of volcanic deposits on Mount Mayon usingdetection of volcanic deposits on Mount Mayon using SAR interferometry Geocarto Int1837ndash41

Canova F Tolomei C Salvi S Toscani G Seno S 2012 Land subsidence along the Ionian coastof SE Sicily (Italy) detection and analysis via Small Baseline Subset (SBAS) multitemporaldifferential SAR interferometry Earth Surf Proc Land 37273ndash286

Caro Cuenca M Hooper AJ Hanssen RF 2011 A new method for temporal phase unwrappingof persistent scatterers InSAR time series IEEE Trans Geosci Remote Sens 494606ndash4615

Cascini L Fornaro G Peduto D 2010 Advanced low- and full-resolution DInSAR map genera-tion for slow-moving landslide analysis at different scales Eng Geol 11229ndash42

Casu F Manzo M Lanari R 2006a A quantitative assessment of the SBAS algorithmperformance for surface deformation retrieval from DInSAR data Remote Sens Environ102195ndash210

Casu F Manzo M Lanari R 2006b Performance analysis of the SBAS algorithm for surfacedeformation retrieval Fringe 2005 Workshop Frascati Italy p 1ndash6

16 OIdreesOI Mohammed et al

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Crosetto M Crippa B 2005 State of the art of land deformation monitoring using differentialSAR interferometry ISPRS Hannover Workshop 2005 High Resolution Earth Imaging forGeospatial Information 2005 May 17ndash20 Hannover

Crosetto M Pasquali P 2008 DSM generation and deformation measurement from SAR dataISPRS J Photogramm Remote Sens 2157ndash167

ESA 2012 ESA Observing the Earth [cited 2013 Jan 18] Available from httpwwwesaintOur_ActivitiesObserving_the_EarthGMESSentinel-3

Fan J Zhao H Tu P Wang Y Guo X Ge D Liu G 2010 CRInSAR for landslide deformationmonitoring a case in threegorge area IEEE IGARSS International Geoscience and RemoteSensing Symposium Honolulu HI p 3956ndash3959

Fernaacutendez J Arjona A Prieto JF Santoyo MA Seco A Monells D Pallero JLG Prieto E LuzoacutenF Mallorquiacute JJ 2009 Application of CPT an advanced DInSAR technique to study surfacedisplacement near Itoiz dam Navarra Spain 95th Journeacutees Luxembourgeoises de Geacuteodyna-mique p 2ndash6

Ferretti A Prati C Rocca F 2000 Nonlinear subsidence rate estimation using permanent scatter-ers in differential SAR interferometry IEEE Trans Geosci Remote Sens 382202ndash2212

Ferretti A Prati A Rocca F Casagli N Farina P Young B 2005 Permanent scatterers technol-ogy a powerful state of the art tool for historic and future monitoring of landslides and otherterrain instability phenomena In Hungr O Fell R Couture R Eberhardt E editors Landsliderisk management Proceedings of the International Conference on landslide risk managementVancouver

Furuya M Mueller K Wahr J 2007 Active salt tectonics in the Needles District Canyonlands(Utah) as detected by interferometric synthetic aperture radar and point target analysis 1992ndash2002 J Geophys Res 1121ndash18

Gini F Lombardini F 2005 Multibaseline cross-track SAR interferometry a signal processingperspective IEEE AampE Syst Mag 2071ndash93

Herrera G Tomaacutes R Lopez-Sanchez JM Delgado J Vicente F Mulas J Cooksley G SanchezeM Duroe J Arnaude A et al 2009 Validation and comparison of advanced differential inter-ferometry techniques Murcia metropolitan area case study ISPRS J Photogramm RemoteSens 64501ndash512

JAXA (2012) Advanced Land Observing Satellite-2 (ALOS-2) Japan Aerospace ExplorationAgency [Internet] [cited 2013 Jan 18] Available from httpwwwjaxajpprojectssatalos2index_ehtml

Domiacutenguez J Romero R Carrasco D Martiacutenez A Mallorquiacute JJ Blanco P Navarrete D 2005Advanced DInSAR based on coherent pixels development and results using cpt techniqueFringe 2005 Workshop Frascati Italy p 1ndash6

Kampes B Adam N 2005 The STUN algorithm for persistent scatterer interferometry FringeATSR Workshop 2005 Advances in SAR Interferometry from ENVISAT and ERS missions2005 28 Novndash2 Dec Frascati Italy

Kampes B Adam N 2006 The STUN algorithm for persistent scatterer interferometry In Lacos-te H editor Fringe 2005 Workshop Frascat ESA Publications Division ESTEC p 1ndash28

Kampes BM Hanssen RF 2004 Ambiguity resolution for permanent scatterer interferometryIEEE Trans Geosci Remote Sens 422446ndash2453

Kenyi LW Kaufmann V 2003 Estimation of rock glacier surface deformation using SAR inter-ferometry data IEEE Trans Geosci Remote Sens 411512ndash1515

Lanari R Casu F Manzo M Lundgren P 2007 Application of the SBAS-DInSAR technique tofault creep a case study of the Hayward fault California Remote Sens Environ 10920ndash28

Lanari R Casu F Manzo M Zeni G Berardino P Manunta M Pepe A 2007 An overview ofthe small baseline subset algorithm a DInSAR technique for surface deformation analysisPure Appl Geophys 164637ndash661

Lauknes TR Dehls J Larsen Y Hoslashgda KA Weydahl DJ 2005 A comparison of SBAS and PSERS INSAR for subsidence monitoring in Oslo Norway Fringe ATSR Workshop 2005Advances in SAR Interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy p 1ndash8

Lee I Chang H-C Ge L 2005 GPS campaigns for validation of InSAR derived DEMs J GlobalPositioning Syst 482ndash87

Lu L Liao M 2008 Subsidence measurement with PS-INSAR techniques in Shanghai Urban IntArch Photogramm Remote Sens Spatial Inf Sci XXXVII173ndash178

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Table 3 Strength and limitations of A-DInSAR techniques

Technique Strength Limitations

PSInSAR bull Not affected by geometricalDoppler centroid nor temporaldecorrelation

bull No restriction regarding the maxi-mum spatial and temporal baselineand Doppler difference

bull Generation of a displacementtrend and changes thereto overtime

bull Measurement in millimetres ofthe lsquoverticalrsquo dimension beingmore accurate than that of GPS

bull Provides a spatial density of mea-surement points not achievablewith conventional techniques

bull Ability to monitor movementwithin discrete temporal subsetsof a full data set

bull Requires large numbers of imageand presence of sufficient numberof PS

bull Target motion must be slowenough to avoid aliasing

bull Precision depends on processingall images at once difficult to inferthe PS distribution in an area with-out significant data processing

bull Interferograms can only be gener-ated from SAR data acquired bythe same satellite

bull If prior information on groundmotion is unavailable phase-unwrapping problems (phase ali-asing) limit the maximum dis-placement between twoconsecutive acquisitions to lessthan 14mm

IPTA bull Use fewer numbers of SAR data

bull Use multiple master images

bull Combination of small baselineimages reduces the impacts ofDEM error and spatial decorrela-tion

bull Multi sensors processing

bull Performance of multiple-imageprocessing l requires individualinterferogram generation

bull Not suitable for non-linear defor-mation history retriever

bull Impossible to define a propagationerror function as a result of diffi-culty in phase unwrapping

SBAS bull Increases sampling rate and pro-vides spatially dense deformationmaps

bull Carry out analysis at low and fullspatial resolutions

bull Suitable for small-scale analysisover wide area

bull Jointly process multi-sensor SARdata collected by different radarsystems

bull Inversion of the unwrapped interf-erogarms using SDV methodallows merging information fromcomputed interferograms andreduces the effect of topographicartefacts present in the DEM usedto compute the differential inter-ferograms

bull Detect and remove the atmo-spheric artefacts and the orbitalramps using the available timespace information

bull Rely on SAR data with small spa-tial and temporal baselines

bull Highly sensitive to geometric dec-orrelation

bull Direct phase unwrapping repre-sents a potential source of errors

(Continued)

12 OIdreesOI Mohammed et al

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Table 3 (Continued)

Technique Strength Limitations

SPN bull Multiple-master image capable ofmeasuring wide area

bull Less sensitive to geometric decor-relation than the SBAS

bull Multi-sensor SAR data processing

bull Provides parameters for checkingthe quality of deformation esti-mates

bull A linear deformation model tounwrap the phases so need notdirectly unwrapping the differen-tial interferograms which is apotential source of errors

bull Precise geocoding of the DInSARproducts based on estimated resid-ual topographic errors

bull Displacement evolution can bevisualized from time-series chart

bull Efficient for urban semi-urbanand rural areas at great detail

bull Can estimate both linear and non-linear deformation components

bull Result depends on the distancefrom reference point

bull Product precision depends on theelectromagnetic stability of thePS the number of available SARimages and the quality of the lin-ear model

STUN bull Reduces number of incorrectlyestimated ambiguity

bull Automatic detection of incorrectlyprocessed interferograms

bull Identify PS with limited numberof observations

bull Stochastic model adjusts differentweight for interferometric datatherefore provides realistic qualityof estimates

bull Precision decreases the furtheraway from reference points due todifference in atmospheric signalwith distance

CPT bull Linear and non-linear deformationcan be extracted

bull Reduces DEM error and atmo-spheric artefacts

bull More reliable and robust whendealing with low number of inter-ferograms

bull The establishing master image isnot required

bull Does not require phase-unwrap-ping process

bull CPT can be carried out withoutDEM

bull Averaging of interferogramsreduces the spatial resolution

bull Isolated single-point targets aremasked out

bull Low coherence limits the numberof pixels that can be included inthe processing

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them against independent or reference data source assumed to be correct Presentlyresearch effort on A-DInSAR techniques for deformation monitoring is being muchmore focused on algorithm and sensor development to enhance the data sampling thanvalidation Notwithstanding the research community is also concerned with the reliabil-ity of the measurements obtained from those algorithms The project initiated by ESAfollowing a recommendation made during the Fringe 2003 Workshop to assess the reli-ability and dependability of PSI methods code named PSIC4 (Persistent Scatterer Inter-ferometry Codes Cross-Comparison and Certification for long-term differentialinterferometry) is the only major research effort targeted at comparison and validation(Crosetto and Crippa 2005 Herrera et al 2009)

Few of the validation attempts basically compared DInSAR result with the result ofdifferent reference data such as levelling GPS observation extensometer and otherin situ data previously conducted One can see that as opposed to other remote sensingapplications like change detection where established statistical techniques for validatingclassifications are in place validating DInSAR derived data-set is yet to attain maturityBesides the work of Lu and Liao (2008) that reports validating DInSAR data with lev-elling using the nearest neighbouring method all others are based on non-rigorous mea-sure of central tendency and dispersion that is the mean and standard deviationAnother point is that inadequate ground truth data impede the efforts in providing com-prehensive scheme or framework for validation purposes

Variation in human interpretation can have a significant impact on what is consid-ered correct Although quite interesting results are reported in most cases it can beobserved that interpretation does not go beyond simple qualitative and quantitative anal-ysis without a comprehensive explanation of the underlying processes behind theresults This could be as a result of inadequate background knowledge about the causesof most of the deformation patterns observed or lack of established standards for inter-preting results within the research community Or again could be as a result of thecomplexity associated with interpreting some deformation phenomenon such as faultcreep

6 Current issues

In this Section we highlight the recent developments in interferometry SAR technologyOur discussion focuses on recent issues on satellite sensors and algorithm developmentSAR satellites for data acquisition are still few in number limiting the amount of dataavailable Aside the fewer satellite sensors for SAR data collection the early generationof sensors such as ERS-12 ASAR-ENVISAR and RADARSAT-12 employing the C-band of microwave portion is the most exploited SAR data sources for deformationmonitoring The low penetrability of C-band limits its success especially in vegetatedareas

61 Sensor development

TanDEM-X (2010) TeraSAR-X and Cosmo-SkyMed (2007) using the X-band andALOS-PALSAR (2006) exploiting the P-band are recent satellite missions employinglonger microwave signals These SAR sensors are expected to offer significant improve-ment in deformation measurement and also to widen the application domain of SARdata

14 OIdreesOI Mohammed et al

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Future SAR satellite missions include ALOS-2 PALSA-2 by JAXA employing L-band and SENTINEL-1 sensor by ESA employing C-band which are both planned tobe launched in 2013 (ESA 2012 JAXA 2012 Mouginot et al 2012)

Also in the area of satellite sensor design researchers are exploring the use of a sin-gle pass with different look angles for InSAR data collection Consequently this willreduce the temporal and spatial baseline to almost zero and make it more applicable forprecise monitoring of water body displacement and the direction of flow

62 Development in algorithm

As mentioned in Section 3 four new A-DInSAR algorithms have evolved in recenttimes in response to the complex nature of some deformation phenomena and the needfor obtaining better coherence in glacier and highly vegetated areas These new algo-rithms are CRInSAR ATI liqui-InSAR and squeeSAR CRInSAR is purposelydesigned as a powerful tool in vegetated areas It involves the installation of man-madereflector in study area to provide persistent scatterer over time series for deformationmonitoring ATI use for detecting and measuring moving objects with SAR techniquesis usually referred to as ground moving target indication It allows for a much betterdetection of moving objects than single-channel SARs hence its growing application intraffic monitoring

A novel achievement in A-DInSAR research for deformation monitoring isliqui-InSAR algorithm developed for monitoring aquatic bodies The technique allowsfor generating fringe patterns on the water bodies using SAR images with a temporalbaseline of maximum 16 s SqueeSAR is another new technique that squeezes theeffects of both persistent and distributed scatterers (DS) SqueeSAR enables the detec-tion of movement in areas dominated by DS It should be noted that these techniquesare still evolving and hence limited to research applications for refinement and evalua-tion of their performances Lastly in the list new development in the algorithm is theFuzzy B-Spline algorithm It was experimented with a group of researchers (Marghany2011) in Universiti Teknologi Malaysia for mapping 3D coastal geomorphology recon-struction The result indicates its potential for measuring ocean fringes and other aquaticgeomorphological characteristics

7 Conclusion

The capabilities of space-borne interferometry SAR data have generated greatenthusiasm over the prospect of establishing remote sensing-based system forcontinuous surface deformation monitoring Today it is well established that a numberof algorithms can be used to estimate deformation time series on land surface and otherbodies from SAR images The result of this study shows that more robust methods andsatellite sensor development design are receiving the attention of researchers to furtherreduce the inherent limitations of the technology

Although all of the A-DInSAR techniques had been applied to different deformationphenomena using data sources from different sensors considerations from the reviewlead to the following conclusions

(1) The suitability of a technique for surface deformation depends on many factorssuch as the number of SAR data available nature and extent of the terraindeformation result expected and the nature of the deformation under study

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(2) Poor performance in some areas is due to low coherence caused by the interac-tion of the radar signal to the surface (like vegetation glacier) the speed of theobject under study and temporal and spatial decorrelations

(3) Improvement in algorithm and sensors to reduce spatial and temporal baseline isbringing the techniques into a state of convergence for enhancement

(4) Again point 3 above and the capability to merge data from different sensors intoone processing module (as in the case of SBAS CPT and SPN) make themmore versatile

(5) More research is needed to establish rigorous statistically founded methods ofvalidation of the result

(6) Availability of more SAR sensors collecting data at longer wavelength will pro-vide the research community with more data for processing and better results

ReferencesAllen TR Wanga Y Gore B 2013 Coastal wetland mapping combining multi-date SAR and

LiDAR Geocarto Int [16 pp advance online publication] doi101080101060492013768297

Arjona A Monells D Fernandez J Duque S Mallorqui J 2010 Deformation analysis employingthe coherent pixel technique and ENVISAT and ERS images in Canary Islands Fringe 2009Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol 2009

Arjona A Santoyo MA Fernaacutendez J Monells D Prieto JF Pallero JLG Prieto E Seco A LuzoacutenF Mallorquiacute J 2010 On the applicability of an advanced dinsar technique near Itoiz and YesaReservoirs Navarra Spain Fringe 2009 Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol2009 p 2ndash7

Bamler R Kampes B Adam N Suchandt S 2005 Assessment of slow deformations and rapidmotions by radar interferometry In Fritsch D editor Photogrammetric week 05 HeidelbergWichmann Verlag p 1ndash12

Bhattacharya A Arora MK Sharma ML 2012 Usefulness of synthetic aperture radar (SAR)interferometry for digital elevation model (DEM) generation and estimation of land surfacedisplacement in Jharia coal field area Geocarto Int 2737ndash41

Blanco-Sanchez P Duque S Mallorqui JJ Monells D 2007 Analysis of highly non-linear defor-mations due to mining activity with dinsar PSIC4 test site Envisat Symposium 2007 2007April 23ndash27 Montreux Switzerland

Blanco-Sanchez P Mallorqui JJ Duque S Monells D 2008 The coherent pixels technique(CPT) an advanced dinsar technique for nonlinear deformation monitoring Pure Appl Geo-phys 1651167ndash1193

Calograve F 2012 Doris project the european downstream service forlandslides and subsidence riskmanagement Geoscience and Remote Sensing Symposium (IGARSS) 2012 IEEE Interna-tional 2012 July 22ndash27 Naples Italy p 3018ndash3021

Canisius J Honda K Tokunaga M 2003 Detection of volcanic deposits on Mount Mayon usingdetection of volcanic deposits on Mount Mayon using SAR interferometry Geocarto Int1837ndash41

Canova F Tolomei C Salvi S Toscani G Seno S 2012 Land subsidence along the Ionian coastof SE Sicily (Italy) detection and analysis via Small Baseline Subset (SBAS) multitemporaldifferential SAR interferometry Earth Surf Proc Land 37273ndash286

Caro Cuenca M Hooper AJ Hanssen RF 2011 A new method for temporal phase unwrappingof persistent scatterers InSAR time series IEEE Trans Geosci Remote Sens 494606ndash4615

Cascini L Fornaro G Peduto D 2010 Advanced low- and full-resolution DInSAR map genera-tion for slow-moving landslide analysis at different scales Eng Geol 11229ndash42

Casu F Manzo M Lanari R 2006a A quantitative assessment of the SBAS algorithmperformance for surface deformation retrieval from DInSAR data Remote Sens Environ102195ndash210

Casu F Manzo M Lanari R 2006b Performance analysis of the SBAS algorithm for surfacedeformation retrieval Fringe 2005 Workshop Frascati Italy p 1ndash6

16 OIdreesOI Mohammed et al

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Crosetto M Crippa B 2005 State of the art of land deformation monitoring using differentialSAR interferometry ISPRS Hannover Workshop 2005 High Resolution Earth Imaging forGeospatial Information 2005 May 17ndash20 Hannover

Crosetto M Pasquali P 2008 DSM generation and deformation measurement from SAR dataISPRS J Photogramm Remote Sens 2157ndash167

ESA 2012 ESA Observing the Earth [cited 2013 Jan 18] Available from httpwwwesaintOur_ActivitiesObserving_the_EarthGMESSentinel-3

Fan J Zhao H Tu P Wang Y Guo X Ge D Liu G 2010 CRInSAR for landslide deformationmonitoring a case in threegorge area IEEE IGARSS International Geoscience and RemoteSensing Symposium Honolulu HI p 3956ndash3959

Fernaacutendez J Arjona A Prieto JF Santoyo MA Seco A Monells D Pallero JLG Prieto E LuzoacutenF Mallorquiacute JJ 2009 Application of CPT an advanced DInSAR technique to study surfacedisplacement near Itoiz dam Navarra Spain 95th Journeacutees Luxembourgeoises de Geacuteodyna-mique p 2ndash6

Ferretti A Prati C Rocca F 2000 Nonlinear subsidence rate estimation using permanent scatter-ers in differential SAR interferometry IEEE Trans Geosci Remote Sens 382202ndash2212

Ferretti A Prati A Rocca F Casagli N Farina P Young B 2005 Permanent scatterers technol-ogy a powerful state of the art tool for historic and future monitoring of landslides and otherterrain instability phenomena In Hungr O Fell R Couture R Eberhardt E editors Landsliderisk management Proceedings of the International Conference on landslide risk managementVancouver

Furuya M Mueller K Wahr J 2007 Active salt tectonics in the Needles District Canyonlands(Utah) as detected by interferometric synthetic aperture radar and point target analysis 1992ndash2002 J Geophys Res 1121ndash18

Gini F Lombardini F 2005 Multibaseline cross-track SAR interferometry a signal processingperspective IEEE AampE Syst Mag 2071ndash93

Herrera G Tomaacutes R Lopez-Sanchez JM Delgado J Vicente F Mulas J Cooksley G SanchezeM Duroe J Arnaude A et al 2009 Validation and comparison of advanced differential inter-ferometry techniques Murcia metropolitan area case study ISPRS J Photogramm RemoteSens 64501ndash512

JAXA (2012) Advanced Land Observing Satellite-2 (ALOS-2) Japan Aerospace ExplorationAgency [Internet] [cited 2013 Jan 18] Available from httpwwwjaxajpprojectssatalos2index_ehtml

Domiacutenguez J Romero R Carrasco D Martiacutenez A Mallorquiacute JJ Blanco P Navarrete D 2005Advanced DInSAR based on coherent pixels development and results using cpt techniqueFringe 2005 Workshop Frascati Italy p 1ndash6

Kampes B Adam N 2005 The STUN algorithm for persistent scatterer interferometry FringeATSR Workshop 2005 Advances in SAR Interferometry from ENVISAT and ERS missions2005 28 Novndash2 Dec Frascati Italy

Kampes B Adam N 2006 The STUN algorithm for persistent scatterer interferometry In Lacos-te H editor Fringe 2005 Workshop Frascat ESA Publications Division ESTEC p 1ndash28

Kampes BM Hanssen RF 2004 Ambiguity resolution for permanent scatterer interferometryIEEE Trans Geosci Remote Sens 422446ndash2453

Kenyi LW Kaufmann V 2003 Estimation of rock glacier surface deformation using SAR inter-ferometry data IEEE Trans Geosci Remote Sens 411512ndash1515

Lanari R Casu F Manzo M Lundgren P 2007 Application of the SBAS-DInSAR technique tofault creep a case study of the Hayward fault California Remote Sens Environ 10920ndash28

Lanari R Casu F Manzo M Zeni G Berardino P Manunta M Pepe A 2007 An overview ofthe small baseline subset algorithm a DInSAR technique for surface deformation analysisPure Appl Geophys 164637ndash661

Lauknes TR Dehls J Larsen Y Hoslashgda KA Weydahl DJ 2005 A comparison of SBAS and PSERS INSAR for subsidence monitoring in Oslo Norway Fringe ATSR Workshop 2005Advances in SAR Interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy p 1ndash8

Lee I Chang H-C Ge L 2005 GPS campaigns for validation of InSAR derived DEMs J GlobalPositioning Syst 482ndash87

Lu L Liao M 2008 Subsidence measurement with PS-INSAR techniques in Shanghai Urban IntArch Photogramm Remote Sens Spatial Inf Sci XXXVII173ndash178

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Table 3 (Continued)

Technique Strength Limitations

SPN bull Multiple-master image capable ofmeasuring wide area

bull Less sensitive to geometric decor-relation than the SBAS

bull Multi-sensor SAR data processing

bull Provides parameters for checkingthe quality of deformation esti-mates

bull A linear deformation model tounwrap the phases so need notdirectly unwrapping the differen-tial interferograms which is apotential source of errors

bull Precise geocoding of the DInSARproducts based on estimated resid-ual topographic errors

bull Displacement evolution can bevisualized from time-series chart

bull Efficient for urban semi-urbanand rural areas at great detail

bull Can estimate both linear and non-linear deformation components

bull Result depends on the distancefrom reference point

bull Product precision depends on theelectromagnetic stability of thePS the number of available SARimages and the quality of the lin-ear model

STUN bull Reduces number of incorrectlyestimated ambiguity

bull Automatic detection of incorrectlyprocessed interferograms

bull Identify PS with limited numberof observations

bull Stochastic model adjusts differentweight for interferometric datatherefore provides realistic qualityof estimates

bull Precision decreases the furtheraway from reference points due todifference in atmospheric signalwith distance

CPT bull Linear and non-linear deformationcan be extracted

bull Reduces DEM error and atmo-spheric artefacts

bull More reliable and robust whendealing with low number of inter-ferograms

bull The establishing master image isnot required

bull Does not require phase-unwrap-ping process

bull CPT can be carried out withoutDEM

bull Averaging of interferogramsreduces the spatial resolution

bull Isolated single-point targets aremasked out

bull Low coherence limits the numberof pixels that can be included inthe processing

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them against independent or reference data source assumed to be correct Presentlyresearch effort on A-DInSAR techniques for deformation monitoring is being muchmore focused on algorithm and sensor development to enhance the data sampling thanvalidation Notwithstanding the research community is also concerned with the reliabil-ity of the measurements obtained from those algorithms The project initiated by ESAfollowing a recommendation made during the Fringe 2003 Workshop to assess the reli-ability and dependability of PSI methods code named PSIC4 (Persistent Scatterer Inter-ferometry Codes Cross-Comparison and Certification for long-term differentialinterferometry) is the only major research effort targeted at comparison and validation(Crosetto and Crippa 2005 Herrera et al 2009)

Few of the validation attempts basically compared DInSAR result with the result ofdifferent reference data such as levelling GPS observation extensometer and otherin situ data previously conducted One can see that as opposed to other remote sensingapplications like change detection where established statistical techniques for validatingclassifications are in place validating DInSAR derived data-set is yet to attain maturityBesides the work of Lu and Liao (2008) that reports validating DInSAR data with lev-elling using the nearest neighbouring method all others are based on non-rigorous mea-sure of central tendency and dispersion that is the mean and standard deviationAnother point is that inadequate ground truth data impede the efforts in providing com-prehensive scheme or framework for validation purposes

Variation in human interpretation can have a significant impact on what is consid-ered correct Although quite interesting results are reported in most cases it can beobserved that interpretation does not go beyond simple qualitative and quantitative anal-ysis without a comprehensive explanation of the underlying processes behind theresults This could be as a result of inadequate background knowledge about the causesof most of the deformation patterns observed or lack of established standards for inter-preting results within the research community Or again could be as a result of thecomplexity associated with interpreting some deformation phenomenon such as faultcreep

6 Current issues

In this Section we highlight the recent developments in interferometry SAR technologyOur discussion focuses on recent issues on satellite sensors and algorithm developmentSAR satellites for data acquisition are still few in number limiting the amount of dataavailable Aside the fewer satellite sensors for SAR data collection the early generationof sensors such as ERS-12 ASAR-ENVISAR and RADARSAT-12 employing the C-band of microwave portion is the most exploited SAR data sources for deformationmonitoring The low penetrability of C-band limits its success especially in vegetatedareas

61 Sensor development

TanDEM-X (2010) TeraSAR-X and Cosmo-SkyMed (2007) using the X-band andALOS-PALSAR (2006) exploiting the P-band are recent satellite missions employinglonger microwave signals These SAR sensors are expected to offer significant improve-ment in deformation measurement and also to widen the application domain of SARdata

14 OIdreesOI Mohammed et al

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Future SAR satellite missions include ALOS-2 PALSA-2 by JAXA employing L-band and SENTINEL-1 sensor by ESA employing C-band which are both planned tobe launched in 2013 (ESA 2012 JAXA 2012 Mouginot et al 2012)

Also in the area of satellite sensor design researchers are exploring the use of a sin-gle pass with different look angles for InSAR data collection Consequently this willreduce the temporal and spatial baseline to almost zero and make it more applicable forprecise monitoring of water body displacement and the direction of flow

62 Development in algorithm

As mentioned in Section 3 four new A-DInSAR algorithms have evolved in recenttimes in response to the complex nature of some deformation phenomena and the needfor obtaining better coherence in glacier and highly vegetated areas These new algo-rithms are CRInSAR ATI liqui-InSAR and squeeSAR CRInSAR is purposelydesigned as a powerful tool in vegetated areas It involves the installation of man-madereflector in study area to provide persistent scatterer over time series for deformationmonitoring ATI use for detecting and measuring moving objects with SAR techniquesis usually referred to as ground moving target indication It allows for a much betterdetection of moving objects than single-channel SARs hence its growing application intraffic monitoring

A novel achievement in A-DInSAR research for deformation monitoring isliqui-InSAR algorithm developed for monitoring aquatic bodies The technique allowsfor generating fringe patterns on the water bodies using SAR images with a temporalbaseline of maximum 16 s SqueeSAR is another new technique that squeezes theeffects of both persistent and distributed scatterers (DS) SqueeSAR enables the detec-tion of movement in areas dominated by DS It should be noted that these techniquesare still evolving and hence limited to research applications for refinement and evalua-tion of their performances Lastly in the list new development in the algorithm is theFuzzy B-Spline algorithm It was experimented with a group of researchers (Marghany2011) in Universiti Teknologi Malaysia for mapping 3D coastal geomorphology recon-struction The result indicates its potential for measuring ocean fringes and other aquaticgeomorphological characteristics

7 Conclusion

The capabilities of space-borne interferometry SAR data have generated greatenthusiasm over the prospect of establishing remote sensing-based system forcontinuous surface deformation monitoring Today it is well established that a numberof algorithms can be used to estimate deformation time series on land surface and otherbodies from SAR images The result of this study shows that more robust methods andsatellite sensor development design are receiving the attention of researchers to furtherreduce the inherent limitations of the technology

Although all of the A-DInSAR techniques had been applied to different deformationphenomena using data sources from different sensors considerations from the reviewlead to the following conclusions

(1) The suitability of a technique for surface deformation depends on many factorssuch as the number of SAR data available nature and extent of the terraindeformation result expected and the nature of the deformation under study

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(2) Poor performance in some areas is due to low coherence caused by the interac-tion of the radar signal to the surface (like vegetation glacier) the speed of theobject under study and temporal and spatial decorrelations

(3) Improvement in algorithm and sensors to reduce spatial and temporal baseline isbringing the techniques into a state of convergence for enhancement

(4) Again point 3 above and the capability to merge data from different sensors intoone processing module (as in the case of SBAS CPT and SPN) make themmore versatile

(5) More research is needed to establish rigorous statistically founded methods ofvalidation of the result

(6) Availability of more SAR sensors collecting data at longer wavelength will pro-vide the research community with more data for processing and better results

ReferencesAllen TR Wanga Y Gore B 2013 Coastal wetland mapping combining multi-date SAR and

LiDAR Geocarto Int [16 pp advance online publication] doi101080101060492013768297

Arjona A Monells D Fernandez J Duque S Mallorqui J 2010 Deformation analysis employingthe coherent pixel technique and ENVISAT and ERS images in Canary Islands Fringe 2009Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol 2009

Arjona A Santoyo MA Fernaacutendez J Monells D Prieto JF Pallero JLG Prieto E Seco A LuzoacutenF Mallorquiacute J 2010 On the applicability of an advanced dinsar technique near Itoiz and YesaReservoirs Navarra Spain Fringe 2009 Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol2009 p 2ndash7

Bamler R Kampes B Adam N Suchandt S 2005 Assessment of slow deformations and rapidmotions by radar interferometry In Fritsch D editor Photogrammetric week 05 HeidelbergWichmann Verlag p 1ndash12

Bhattacharya A Arora MK Sharma ML 2012 Usefulness of synthetic aperture radar (SAR)interferometry for digital elevation model (DEM) generation and estimation of land surfacedisplacement in Jharia coal field area Geocarto Int 2737ndash41

Blanco-Sanchez P Duque S Mallorqui JJ Monells D 2007 Analysis of highly non-linear defor-mations due to mining activity with dinsar PSIC4 test site Envisat Symposium 2007 2007April 23ndash27 Montreux Switzerland

Blanco-Sanchez P Mallorqui JJ Duque S Monells D 2008 The coherent pixels technique(CPT) an advanced dinsar technique for nonlinear deformation monitoring Pure Appl Geo-phys 1651167ndash1193

Calograve F 2012 Doris project the european downstream service forlandslides and subsidence riskmanagement Geoscience and Remote Sensing Symposium (IGARSS) 2012 IEEE Interna-tional 2012 July 22ndash27 Naples Italy p 3018ndash3021

Canisius J Honda K Tokunaga M 2003 Detection of volcanic deposits on Mount Mayon usingdetection of volcanic deposits on Mount Mayon using SAR interferometry Geocarto Int1837ndash41

Canova F Tolomei C Salvi S Toscani G Seno S 2012 Land subsidence along the Ionian coastof SE Sicily (Italy) detection and analysis via Small Baseline Subset (SBAS) multitemporaldifferential SAR interferometry Earth Surf Proc Land 37273ndash286

Caro Cuenca M Hooper AJ Hanssen RF 2011 A new method for temporal phase unwrappingof persistent scatterers InSAR time series IEEE Trans Geosci Remote Sens 494606ndash4615

Cascini L Fornaro G Peduto D 2010 Advanced low- and full-resolution DInSAR map genera-tion for slow-moving landslide analysis at different scales Eng Geol 11229ndash42

Casu F Manzo M Lanari R 2006a A quantitative assessment of the SBAS algorithmperformance for surface deformation retrieval from DInSAR data Remote Sens Environ102195ndash210

Casu F Manzo M Lanari R 2006b Performance analysis of the SBAS algorithm for surfacedeformation retrieval Fringe 2005 Workshop Frascati Italy p 1ndash6

16 OIdreesOI Mohammed et al

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Crosetto M Crippa B 2005 State of the art of land deformation monitoring using differentialSAR interferometry ISPRS Hannover Workshop 2005 High Resolution Earth Imaging forGeospatial Information 2005 May 17ndash20 Hannover

Crosetto M Pasquali P 2008 DSM generation and deformation measurement from SAR dataISPRS J Photogramm Remote Sens 2157ndash167

ESA 2012 ESA Observing the Earth [cited 2013 Jan 18] Available from httpwwwesaintOur_ActivitiesObserving_the_EarthGMESSentinel-3

Fan J Zhao H Tu P Wang Y Guo X Ge D Liu G 2010 CRInSAR for landslide deformationmonitoring a case in threegorge area IEEE IGARSS International Geoscience and RemoteSensing Symposium Honolulu HI p 3956ndash3959

Fernaacutendez J Arjona A Prieto JF Santoyo MA Seco A Monells D Pallero JLG Prieto E LuzoacutenF Mallorquiacute JJ 2009 Application of CPT an advanced DInSAR technique to study surfacedisplacement near Itoiz dam Navarra Spain 95th Journeacutees Luxembourgeoises de Geacuteodyna-mique p 2ndash6

Ferretti A Prati C Rocca F 2000 Nonlinear subsidence rate estimation using permanent scatter-ers in differential SAR interferometry IEEE Trans Geosci Remote Sens 382202ndash2212

Ferretti A Prati A Rocca F Casagli N Farina P Young B 2005 Permanent scatterers technol-ogy a powerful state of the art tool for historic and future monitoring of landslides and otherterrain instability phenomena In Hungr O Fell R Couture R Eberhardt E editors Landsliderisk management Proceedings of the International Conference on landslide risk managementVancouver

Furuya M Mueller K Wahr J 2007 Active salt tectonics in the Needles District Canyonlands(Utah) as detected by interferometric synthetic aperture radar and point target analysis 1992ndash2002 J Geophys Res 1121ndash18

Gini F Lombardini F 2005 Multibaseline cross-track SAR interferometry a signal processingperspective IEEE AampE Syst Mag 2071ndash93

Herrera G Tomaacutes R Lopez-Sanchez JM Delgado J Vicente F Mulas J Cooksley G SanchezeM Duroe J Arnaude A et al 2009 Validation and comparison of advanced differential inter-ferometry techniques Murcia metropolitan area case study ISPRS J Photogramm RemoteSens 64501ndash512

JAXA (2012) Advanced Land Observing Satellite-2 (ALOS-2) Japan Aerospace ExplorationAgency [Internet] [cited 2013 Jan 18] Available from httpwwwjaxajpprojectssatalos2index_ehtml

Domiacutenguez J Romero R Carrasco D Martiacutenez A Mallorquiacute JJ Blanco P Navarrete D 2005Advanced DInSAR based on coherent pixels development and results using cpt techniqueFringe 2005 Workshop Frascati Italy p 1ndash6

Kampes B Adam N 2005 The STUN algorithm for persistent scatterer interferometry FringeATSR Workshop 2005 Advances in SAR Interferometry from ENVISAT and ERS missions2005 28 Novndash2 Dec Frascati Italy

Kampes B Adam N 2006 The STUN algorithm for persistent scatterer interferometry In Lacos-te H editor Fringe 2005 Workshop Frascat ESA Publications Division ESTEC p 1ndash28

Kampes BM Hanssen RF 2004 Ambiguity resolution for permanent scatterer interferometryIEEE Trans Geosci Remote Sens 422446ndash2453

Kenyi LW Kaufmann V 2003 Estimation of rock glacier surface deformation using SAR inter-ferometry data IEEE Trans Geosci Remote Sens 411512ndash1515

Lanari R Casu F Manzo M Lundgren P 2007 Application of the SBAS-DInSAR technique tofault creep a case study of the Hayward fault California Remote Sens Environ 10920ndash28

Lanari R Casu F Manzo M Zeni G Berardino P Manunta M Pepe A 2007 An overview ofthe small baseline subset algorithm a DInSAR technique for surface deformation analysisPure Appl Geophys 164637ndash661

Lauknes TR Dehls J Larsen Y Hoslashgda KA Weydahl DJ 2005 A comparison of SBAS and PSERS INSAR for subsidence monitoring in Oslo Norway Fringe ATSR Workshop 2005Advances in SAR Interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy p 1ndash8

Lee I Chang H-C Ge L 2005 GPS campaigns for validation of InSAR derived DEMs J GlobalPositioning Syst 482ndash87

Lu L Liao M 2008 Subsidence measurement with PS-INSAR techniques in Shanghai Urban IntArch Photogramm Remote Sens Spatial Inf Sci XXXVII173ndash178

Geocarto International 17

Geo

cart

o In

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nal

them against independent or reference data source assumed to be correct Presentlyresearch effort on A-DInSAR techniques for deformation monitoring is being muchmore focused on algorithm and sensor development to enhance the data sampling thanvalidation Notwithstanding the research community is also concerned with the reliabil-ity of the measurements obtained from those algorithms The project initiated by ESAfollowing a recommendation made during the Fringe 2003 Workshop to assess the reli-ability and dependability of PSI methods code named PSIC4 (Persistent Scatterer Inter-ferometry Codes Cross-Comparison and Certification for long-term differentialinterferometry) is the only major research effort targeted at comparison and validation(Crosetto and Crippa 2005 Herrera et al 2009)

Few of the validation attempts basically compared DInSAR result with the result ofdifferent reference data such as levelling GPS observation extensometer and otherin situ data previously conducted One can see that as opposed to other remote sensingapplications like change detection where established statistical techniques for validatingclassifications are in place validating DInSAR derived data-set is yet to attain maturityBesides the work of Lu and Liao (2008) that reports validating DInSAR data with lev-elling using the nearest neighbouring method all others are based on non-rigorous mea-sure of central tendency and dispersion that is the mean and standard deviationAnother point is that inadequate ground truth data impede the efforts in providing com-prehensive scheme or framework for validation purposes

Variation in human interpretation can have a significant impact on what is consid-ered correct Although quite interesting results are reported in most cases it can beobserved that interpretation does not go beyond simple qualitative and quantitative anal-ysis without a comprehensive explanation of the underlying processes behind theresults This could be as a result of inadequate background knowledge about the causesof most of the deformation patterns observed or lack of established standards for inter-preting results within the research community Or again could be as a result of thecomplexity associated with interpreting some deformation phenomenon such as faultcreep

6 Current issues

In this Section we highlight the recent developments in interferometry SAR technologyOur discussion focuses on recent issues on satellite sensors and algorithm developmentSAR satellites for data acquisition are still few in number limiting the amount of dataavailable Aside the fewer satellite sensors for SAR data collection the early generationof sensors such as ERS-12 ASAR-ENVISAR and RADARSAT-12 employing the C-band of microwave portion is the most exploited SAR data sources for deformationmonitoring The low penetrability of C-band limits its success especially in vegetatedareas

61 Sensor development

TanDEM-X (2010) TeraSAR-X and Cosmo-SkyMed (2007) using the X-band andALOS-PALSAR (2006) exploiting the P-band are recent satellite missions employinglonger microwave signals These SAR sensors are expected to offer significant improve-ment in deformation measurement and also to widen the application domain of SARdata

14 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

Future SAR satellite missions include ALOS-2 PALSA-2 by JAXA employing L-band and SENTINEL-1 sensor by ESA employing C-band which are both planned tobe launched in 2013 (ESA 2012 JAXA 2012 Mouginot et al 2012)

Also in the area of satellite sensor design researchers are exploring the use of a sin-gle pass with different look angles for InSAR data collection Consequently this willreduce the temporal and spatial baseline to almost zero and make it more applicable forprecise monitoring of water body displacement and the direction of flow

62 Development in algorithm

As mentioned in Section 3 four new A-DInSAR algorithms have evolved in recenttimes in response to the complex nature of some deformation phenomena and the needfor obtaining better coherence in glacier and highly vegetated areas These new algo-rithms are CRInSAR ATI liqui-InSAR and squeeSAR CRInSAR is purposelydesigned as a powerful tool in vegetated areas It involves the installation of man-madereflector in study area to provide persistent scatterer over time series for deformationmonitoring ATI use for detecting and measuring moving objects with SAR techniquesis usually referred to as ground moving target indication It allows for a much betterdetection of moving objects than single-channel SARs hence its growing application intraffic monitoring

A novel achievement in A-DInSAR research for deformation monitoring isliqui-InSAR algorithm developed for monitoring aquatic bodies The technique allowsfor generating fringe patterns on the water bodies using SAR images with a temporalbaseline of maximum 16 s SqueeSAR is another new technique that squeezes theeffects of both persistent and distributed scatterers (DS) SqueeSAR enables the detec-tion of movement in areas dominated by DS It should be noted that these techniquesare still evolving and hence limited to research applications for refinement and evalua-tion of their performances Lastly in the list new development in the algorithm is theFuzzy B-Spline algorithm It was experimented with a group of researchers (Marghany2011) in Universiti Teknologi Malaysia for mapping 3D coastal geomorphology recon-struction The result indicates its potential for measuring ocean fringes and other aquaticgeomorphological characteristics

7 Conclusion

The capabilities of space-borne interferometry SAR data have generated greatenthusiasm over the prospect of establishing remote sensing-based system forcontinuous surface deformation monitoring Today it is well established that a numberof algorithms can be used to estimate deformation time series on land surface and otherbodies from SAR images The result of this study shows that more robust methods andsatellite sensor development design are receiving the attention of researchers to furtherreduce the inherent limitations of the technology

Although all of the A-DInSAR techniques had been applied to different deformationphenomena using data sources from different sensors considerations from the reviewlead to the following conclusions

(1) The suitability of a technique for surface deformation depends on many factorssuch as the number of SAR data available nature and extent of the terraindeformation result expected and the nature of the deformation under study

Geocarto International 15

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(2) Poor performance in some areas is due to low coherence caused by the interac-tion of the radar signal to the surface (like vegetation glacier) the speed of theobject under study and temporal and spatial decorrelations

(3) Improvement in algorithm and sensors to reduce spatial and temporal baseline isbringing the techniques into a state of convergence for enhancement

(4) Again point 3 above and the capability to merge data from different sensors intoone processing module (as in the case of SBAS CPT and SPN) make themmore versatile

(5) More research is needed to establish rigorous statistically founded methods ofvalidation of the result

(6) Availability of more SAR sensors collecting data at longer wavelength will pro-vide the research community with more data for processing and better results

ReferencesAllen TR Wanga Y Gore B 2013 Coastal wetland mapping combining multi-date SAR and

LiDAR Geocarto Int [16 pp advance online publication] doi101080101060492013768297

Arjona A Monells D Fernandez J Duque S Mallorqui J 2010 Deformation analysis employingthe coherent pixel technique and ENVISAT and ERS images in Canary Islands Fringe 2009Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol 2009

Arjona A Santoyo MA Fernaacutendez J Monells D Prieto JF Pallero JLG Prieto E Seco A LuzoacutenF Mallorquiacute J 2010 On the applicability of an advanced dinsar technique near Itoiz and YesaReservoirs Navarra Spain Fringe 2009 Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol2009 p 2ndash7

Bamler R Kampes B Adam N Suchandt S 2005 Assessment of slow deformations and rapidmotions by radar interferometry In Fritsch D editor Photogrammetric week 05 HeidelbergWichmann Verlag p 1ndash12

Bhattacharya A Arora MK Sharma ML 2012 Usefulness of synthetic aperture radar (SAR)interferometry for digital elevation model (DEM) generation and estimation of land surfacedisplacement in Jharia coal field area Geocarto Int 2737ndash41

Blanco-Sanchez P Duque S Mallorqui JJ Monells D 2007 Analysis of highly non-linear defor-mations due to mining activity with dinsar PSIC4 test site Envisat Symposium 2007 2007April 23ndash27 Montreux Switzerland

Blanco-Sanchez P Mallorqui JJ Duque S Monells D 2008 The coherent pixels technique(CPT) an advanced dinsar technique for nonlinear deformation monitoring Pure Appl Geo-phys 1651167ndash1193

Calograve F 2012 Doris project the european downstream service forlandslides and subsidence riskmanagement Geoscience and Remote Sensing Symposium (IGARSS) 2012 IEEE Interna-tional 2012 July 22ndash27 Naples Italy p 3018ndash3021

Canisius J Honda K Tokunaga M 2003 Detection of volcanic deposits on Mount Mayon usingdetection of volcanic deposits on Mount Mayon using SAR interferometry Geocarto Int1837ndash41

Canova F Tolomei C Salvi S Toscani G Seno S 2012 Land subsidence along the Ionian coastof SE Sicily (Italy) detection and analysis via Small Baseline Subset (SBAS) multitemporaldifferential SAR interferometry Earth Surf Proc Land 37273ndash286

Caro Cuenca M Hooper AJ Hanssen RF 2011 A new method for temporal phase unwrappingof persistent scatterers InSAR time series IEEE Trans Geosci Remote Sens 494606ndash4615

Cascini L Fornaro G Peduto D 2010 Advanced low- and full-resolution DInSAR map genera-tion for slow-moving landslide analysis at different scales Eng Geol 11229ndash42

Casu F Manzo M Lanari R 2006a A quantitative assessment of the SBAS algorithmperformance for surface deformation retrieval from DInSAR data Remote Sens Environ102195ndash210

Casu F Manzo M Lanari R 2006b Performance analysis of the SBAS algorithm for surfacedeformation retrieval Fringe 2005 Workshop Frascati Italy p 1ndash6

16 OIdreesOI Mohammed et al

Geo

cart

o In

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atio

nal

Crosetto M Crippa B 2005 State of the art of land deformation monitoring using differentialSAR interferometry ISPRS Hannover Workshop 2005 High Resolution Earth Imaging forGeospatial Information 2005 May 17ndash20 Hannover

Crosetto M Pasquali P 2008 DSM generation and deformation measurement from SAR dataISPRS J Photogramm Remote Sens 2157ndash167

ESA 2012 ESA Observing the Earth [cited 2013 Jan 18] Available from httpwwwesaintOur_ActivitiesObserving_the_EarthGMESSentinel-3

Fan J Zhao H Tu P Wang Y Guo X Ge D Liu G 2010 CRInSAR for landslide deformationmonitoring a case in threegorge area IEEE IGARSS International Geoscience and RemoteSensing Symposium Honolulu HI p 3956ndash3959

Fernaacutendez J Arjona A Prieto JF Santoyo MA Seco A Monells D Pallero JLG Prieto E LuzoacutenF Mallorquiacute JJ 2009 Application of CPT an advanced DInSAR technique to study surfacedisplacement near Itoiz dam Navarra Spain 95th Journeacutees Luxembourgeoises de Geacuteodyna-mique p 2ndash6

Ferretti A Prati C Rocca F 2000 Nonlinear subsidence rate estimation using permanent scatter-ers in differential SAR interferometry IEEE Trans Geosci Remote Sens 382202ndash2212

Ferretti A Prati A Rocca F Casagli N Farina P Young B 2005 Permanent scatterers technol-ogy a powerful state of the art tool for historic and future monitoring of landslides and otherterrain instability phenomena In Hungr O Fell R Couture R Eberhardt E editors Landsliderisk management Proceedings of the International Conference on landslide risk managementVancouver

Furuya M Mueller K Wahr J 2007 Active salt tectonics in the Needles District Canyonlands(Utah) as detected by interferometric synthetic aperture radar and point target analysis 1992ndash2002 J Geophys Res 1121ndash18

Gini F Lombardini F 2005 Multibaseline cross-track SAR interferometry a signal processingperspective IEEE AampE Syst Mag 2071ndash93

Herrera G Tomaacutes R Lopez-Sanchez JM Delgado J Vicente F Mulas J Cooksley G SanchezeM Duroe J Arnaude A et al 2009 Validation and comparison of advanced differential inter-ferometry techniques Murcia metropolitan area case study ISPRS J Photogramm RemoteSens 64501ndash512

JAXA (2012) Advanced Land Observing Satellite-2 (ALOS-2) Japan Aerospace ExplorationAgency [Internet] [cited 2013 Jan 18] Available from httpwwwjaxajpprojectssatalos2index_ehtml

Domiacutenguez J Romero R Carrasco D Martiacutenez A Mallorquiacute JJ Blanco P Navarrete D 2005Advanced DInSAR based on coherent pixels development and results using cpt techniqueFringe 2005 Workshop Frascati Italy p 1ndash6

Kampes B Adam N 2005 The STUN algorithm for persistent scatterer interferometry FringeATSR Workshop 2005 Advances in SAR Interferometry from ENVISAT and ERS missions2005 28 Novndash2 Dec Frascati Italy

Kampes B Adam N 2006 The STUN algorithm for persistent scatterer interferometry In Lacos-te H editor Fringe 2005 Workshop Frascat ESA Publications Division ESTEC p 1ndash28

Kampes BM Hanssen RF 2004 Ambiguity resolution for permanent scatterer interferometryIEEE Trans Geosci Remote Sens 422446ndash2453

Kenyi LW Kaufmann V 2003 Estimation of rock glacier surface deformation using SAR inter-ferometry data IEEE Trans Geosci Remote Sens 411512ndash1515

Lanari R Casu F Manzo M Lundgren P 2007 Application of the SBAS-DInSAR technique tofault creep a case study of the Hayward fault California Remote Sens Environ 10920ndash28

Lanari R Casu F Manzo M Zeni G Berardino P Manunta M Pepe A 2007 An overview ofthe small baseline subset algorithm a DInSAR technique for surface deformation analysisPure Appl Geophys 164637ndash661

Lauknes TR Dehls J Larsen Y Hoslashgda KA Weydahl DJ 2005 A comparison of SBAS and PSERS INSAR for subsidence monitoring in Oslo Norway Fringe ATSR Workshop 2005Advances in SAR Interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy p 1ndash8

Lee I Chang H-C Ge L 2005 GPS campaigns for validation of InSAR derived DEMs J GlobalPositioning Syst 482ndash87

Lu L Liao M 2008 Subsidence measurement with PS-INSAR techniques in Shanghai Urban IntArch Photogramm Remote Sens Spatial Inf Sci XXXVII173ndash178

Geocarto International 17

Geo

cart

o In

tern

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nal

Future SAR satellite missions include ALOS-2 PALSA-2 by JAXA employing L-band and SENTINEL-1 sensor by ESA employing C-band which are both planned tobe launched in 2013 (ESA 2012 JAXA 2012 Mouginot et al 2012)

Also in the area of satellite sensor design researchers are exploring the use of a sin-gle pass with different look angles for InSAR data collection Consequently this willreduce the temporal and spatial baseline to almost zero and make it more applicable forprecise monitoring of water body displacement and the direction of flow

62 Development in algorithm

As mentioned in Section 3 four new A-DInSAR algorithms have evolved in recenttimes in response to the complex nature of some deformation phenomena and the needfor obtaining better coherence in glacier and highly vegetated areas These new algo-rithms are CRInSAR ATI liqui-InSAR and squeeSAR CRInSAR is purposelydesigned as a powerful tool in vegetated areas It involves the installation of man-madereflector in study area to provide persistent scatterer over time series for deformationmonitoring ATI use for detecting and measuring moving objects with SAR techniquesis usually referred to as ground moving target indication It allows for a much betterdetection of moving objects than single-channel SARs hence its growing application intraffic monitoring

A novel achievement in A-DInSAR research for deformation monitoring isliqui-InSAR algorithm developed for monitoring aquatic bodies The technique allowsfor generating fringe patterns on the water bodies using SAR images with a temporalbaseline of maximum 16 s SqueeSAR is another new technique that squeezes theeffects of both persistent and distributed scatterers (DS) SqueeSAR enables the detec-tion of movement in areas dominated by DS It should be noted that these techniquesare still evolving and hence limited to research applications for refinement and evalua-tion of their performances Lastly in the list new development in the algorithm is theFuzzy B-Spline algorithm It was experimented with a group of researchers (Marghany2011) in Universiti Teknologi Malaysia for mapping 3D coastal geomorphology recon-struction The result indicates its potential for measuring ocean fringes and other aquaticgeomorphological characteristics

7 Conclusion

The capabilities of space-borne interferometry SAR data have generated greatenthusiasm over the prospect of establishing remote sensing-based system forcontinuous surface deformation monitoring Today it is well established that a numberof algorithms can be used to estimate deformation time series on land surface and otherbodies from SAR images The result of this study shows that more robust methods andsatellite sensor development design are receiving the attention of researchers to furtherreduce the inherent limitations of the technology

Although all of the A-DInSAR techniques had been applied to different deformationphenomena using data sources from different sensors considerations from the reviewlead to the following conclusions

(1) The suitability of a technique for surface deformation depends on many factorssuch as the number of SAR data available nature and extent of the terraindeformation result expected and the nature of the deformation under study

Geocarto International 15

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(2) Poor performance in some areas is due to low coherence caused by the interac-tion of the radar signal to the surface (like vegetation glacier) the speed of theobject under study and temporal and spatial decorrelations

(3) Improvement in algorithm and sensors to reduce spatial and temporal baseline isbringing the techniques into a state of convergence for enhancement

(4) Again point 3 above and the capability to merge data from different sensors intoone processing module (as in the case of SBAS CPT and SPN) make themmore versatile

(5) More research is needed to establish rigorous statistically founded methods ofvalidation of the result

(6) Availability of more SAR sensors collecting data at longer wavelength will pro-vide the research community with more data for processing and better results

ReferencesAllen TR Wanga Y Gore B 2013 Coastal wetland mapping combining multi-date SAR and

LiDAR Geocarto Int [16 pp advance online publication] doi101080101060492013768297

Arjona A Monells D Fernandez J Duque S Mallorqui J 2010 Deformation analysis employingthe coherent pixel technique and ENVISAT and ERS images in Canary Islands Fringe 2009Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol 2009

Arjona A Santoyo MA Fernaacutendez J Monells D Prieto JF Pallero JLG Prieto E Seco A LuzoacutenF Mallorquiacute J 2010 On the applicability of an advanced dinsar technique near Itoiz and YesaReservoirs Navarra Spain Fringe 2009 Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol2009 p 2ndash7

Bamler R Kampes B Adam N Suchandt S 2005 Assessment of slow deformations and rapidmotions by radar interferometry In Fritsch D editor Photogrammetric week 05 HeidelbergWichmann Verlag p 1ndash12

Bhattacharya A Arora MK Sharma ML 2012 Usefulness of synthetic aperture radar (SAR)interferometry for digital elevation model (DEM) generation and estimation of land surfacedisplacement in Jharia coal field area Geocarto Int 2737ndash41

Blanco-Sanchez P Duque S Mallorqui JJ Monells D 2007 Analysis of highly non-linear defor-mations due to mining activity with dinsar PSIC4 test site Envisat Symposium 2007 2007April 23ndash27 Montreux Switzerland

Blanco-Sanchez P Mallorqui JJ Duque S Monells D 2008 The coherent pixels technique(CPT) an advanced dinsar technique for nonlinear deformation monitoring Pure Appl Geo-phys 1651167ndash1193

Calograve F 2012 Doris project the european downstream service forlandslides and subsidence riskmanagement Geoscience and Remote Sensing Symposium (IGARSS) 2012 IEEE Interna-tional 2012 July 22ndash27 Naples Italy p 3018ndash3021

Canisius J Honda K Tokunaga M 2003 Detection of volcanic deposits on Mount Mayon usingdetection of volcanic deposits on Mount Mayon using SAR interferometry Geocarto Int1837ndash41

Canova F Tolomei C Salvi S Toscani G Seno S 2012 Land subsidence along the Ionian coastof SE Sicily (Italy) detection and analysis via Small Baseline Subset (SBAS) multitemporaldifferential SAR interferometry Earth Surf Proc Land 37273ndash286

Caro Cuenca M Hooper AJ Hanssen RF 2011 A new method for temporal phase unwrappingof persistent scatterers InSAR time series IEEE Trans Geosci Remote Sens 494606ndash4615

Cascini L Fornaro G Peduto D 2010 Advanced low- and full-resolution DInSAR map genera-tion for slow-moving landslide analysis at different scales Eng Geol 11229ndash42

Casu F Manzo M Lanari R 2006a A quantitative assessment of the SBAS algorithmperformance for surface deformation retrieval from DInSAR data Remote Sens Environ102195ndash210

Casu F Manzo M Lanari R 2006b Performance analysis of the SBAS algorithm for surfacedeformation retrieval Fringe 2005 Workshop Frascati Italy p 1ndash6

16 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

Crosetto M Crippa B 2005 State of the art of land deformation monitoring using differentialSAR interferometry ISPRS Hannover Workshop 2005 High Resolution Earth Imaging forGeospatial Information 2005 May 17ndash20 Hannover

Crosetto M Pasquali P 2008 DSM generation and deformation measurement from SAR dataISPRS J Photogramm Remote Sens 2157ndash167

ESA 2012 ESA Observing the Earth [cited 2013 Jan 18] Available from httpwwwesaintOur_ActivitiesObserving_the_EarthGMESSentinel-3

Fan J Zhao H Tu P Wang Y Guo X Ge D Liu G 2010 CRInSAR for landslide deformationmonitoring a case in threegorge area IEEE IGARSS International Geoscience and RemoteSensing Symposium Honolulu HI p 3956ndash3959

Fernaacutendez J Arjona A Prieto JF Santoyo MA Seco A Monells D Pallero JLG Prieto E LuzoacutenF Mallorquiacute JJ 2009 Application of CPT an advanced DInSAR technique to study surfacedisplacement near Itoiz dam Navarra Spain 95th Journeacutees Luxembourgeoises de Geacuteodyna-mique p 2ndash6

Ferretti A Prati C Rocca F 2000 Nonlinear subsidence rate estimation using permanent scatter-ers in differential SAR interferometry IEEE Trans Geosci Remote Sens 382202ndash2212

Ferretti A Prati A Rocca F Casagli N Farina P Young B 2005 Permanent scatterers technol-ogy a powerful state of the art tool for historic and future monitoring of landslides and otherterrain instability phenomena In Hungr O Fell R Couture R Eberhardt E editors Landsliderisk management Proceedings of the International Conference on landslide risk managementVancouver

Furuya M Mueller K Wahr J 2007 Active salt tectonics in the Needles District Canyonlands(Utah) as detected by interferometric synthetic aperture radar and point target analysis 1992ndash2002 J Geophys Res 1121ndash18

Gini F Lombardini F 2005 Multibaseline cross-track SAR interferometry a signal processingperspective IEEE AampE Syst Mag 2071ndash93

Herrera G Tomaacutes R Lopez-Sanchez JM Delgado J Vicente F Mulas J Cooksley G SanchezeM Duroe J Arnaude A et al 2009 Validation and comparison of advanced differential inter-ferometry techniques Murcia metropolitan area case study ISPRS J Photogramm RemoteSens 64501ndash512

JAXA (2012) Advanced Land Observing Satellite-2 (ALOS-2) Japan Aerospace ExplorationAgency [Internet] [cited 2013 Jan 18] Available from httpwwwjaxajpprojectssatalos2index_ehtml

Domiacutenguez J Romero R Carrasco D Martiacutenez A Mallorquiacute JJ Blanco P Navarrete D 2005Advanced DInSAR based on coherent pixels development and results using cpt techniqueFringe 2005 Workshop Frascati Italy p 1ndash6

Kampes B Adam N 2005 The STUN algorithm for persistent scatterer interferometry FringeATSR Workshop 2005 Advances in SAR Interferometry from ENVISAT and ERS missions2005 28 Novndash2 Dec Frascati Italy

Kampes B Adam N 2006 The STUN algorithm for persistent scatterer interferometry In Lacos-te H editor Fringe 2005 Workshop Frascat ESA Publications Division ESTEC p 1ndash28

Kampes BM Hanssen RF 2004 Ambiguity resolution for permanent scatterer interferometryIEEE Trans Geosci Remote Sens 422446ndash2453

Kenyi LW Kaufmann V 2003 Estimation of rock glacier surface deformation using SAR inter-ferometry data IEEE Trans Geosci Remote Sens 411512ndash1515

Lanari R Casu F Manzo M Lundgren P 2007 Application of the SBAS-DInSAR technique tofault creep a case study of the Hayward fault California Remote Sens Environ 10920ndash28

Lanari R Casu F Manzo M Zeni G Berardino P Manunta M Pepe A 2007 An overview ofthe small baseline subset algorithm a DInSAR technique for surface deformation analysisPure Appl Geophys 164637ndash661

Lauknes TR Dehls J Larsen Y Hoslashgda KA Weydahl DJ 2005 A comparison of SBAS and PSERS INSAR for subsidence monitoring in Oslo Norway Fringe ATSR Workshop 2005Advances in SAR Interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy p 1ndash8

Lee I Chang H-C Ge L 2005 GPS campaigns for validation of InSAR derived DEMs J GlobalPositioning Syst 482ndash87

Lu L Liao M 2008 Subsidence measurement with PS-INSAR techniques in Shanghai Urban IntArch Photogramm Remote Sens Spatial Inf Sci XXXVII173ndash178

Geocarto International 17

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cart

o In

tern

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nal

(2) Poor performance in some areas is due to low coherence caused by the interac-tion of the radar signal to the surface (like vegetation glacier) the speed of theobject under study and temporal and spatial decorrelations

(3) Improvement in algorithm and sensors to reduce spatial and temporal baseline isbringing the techniques into a state of convergence for enhancement

(4) Again point 3 above and the capability to merge data from different sensors intoone processing module (as in the case of SBAS CPT and SPN) make themmore versatile

(5) More research is needed to establish rigorous statistically founded methods ofvalidation of the result

(6) Availability of more SAR sensors collecting data at longer wavelength will pro-vide the research community with more data for processing and better results

ReferencesAllen TR Wanga Y Gore B 2013 Coastal wetland mapping combining multi-date SAR and

LiDAR Geocarto Int [16 pp advance online publication] doi101080101060492013768297

Arjona A Monells D Fernandez J Duque S Mallorqui J 2010 Deformation analysis employingthe coherent pixel technique and ENVISAT and ERS images in Canary Islands Fringe 2009Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol 2009

Arjona A Santoyo MA Fernaacutendez J Monells D Prieto JF Pallero JLG Prieto E Seco A LuzoacutenF Mallorquiacute J 2010 On the applicability of an advanced dinsar technique near Itoiz and YesaReservoirs Navarra Spain Fringe 2009 Workshop 30 Novndash4 Dec 2009 Frascati Italy Vol2009 p 2ndash7

Bamler R Kampes B Adam N Suchandt S 2005 Assessment of slow deformations and rapidmotions by radar interferometry In Fritsch D editor Photogrammetric week 05 HeidelbergWichmann Verlag p 1ndash12

Bhattacharya A Arora MK Sharma ML 2012 Usefulness of synthetic aperture radar (SAR)interferometry for digital elevation model (DEM) generation and estimation of land surfacedisplacement in Jharia coal field area Geocarto Int 2737ndash41

Blanco-Sanchez P Duque S Mallorqui JJ Monells D 2007 Analysis of highly non-linear defor-mations due to mining activity with dinsar PSIC4 test site Envisat Symposium 2007 2007April 23ndash27 Montreux Switzerland

Blanco-Sanchez P Mallorqui JJ Duque S Monells D 2008 The coherent pixels technique(CPT) an advanced dinsar technique for nonlinear deformation monitoring Pure Appl Geo-phys 1651167ndash1193

Calograve F 2012 Doris project the european downstream service forlandslides and subsidence riskmanagement Geoscience and Remote Sensing Symposium (IGARSS) 2012 IEEE Interna-tional 2012 July 22ndash27 Naples Italy p 3018ndash3021

Canisius J Honda K Tokunaga M 2003 Detection of volcanic deposits on Mount Mayon usingdetection of volcanic deposits on Mount Mayon using SAR interferometry Geocarto Int1837ndash41

Canova F Tolomei C Salvi S Toscani G Seno S 2012 Land subsidence along the Ionian coastof SE Sicily (Italy) detection and analysis via Small Baseline Subset (SBAS) multitemporaldifferential SAR interferometry Earth Surf Proc Land 37273ndash286

Caro Cuenca M Hooper AJ Hanssen RF 2011 A new method for temporal phase unwrappingof persistent scatterers InSAR time series IEEE Trans Geosci Remote Sens 494606ndash4615

Cascini L Fornaro G Peduto D 2010 Advanced low- and full-resolution DInSAR map genera-tion for slow-moving landslide analysis at different scales Eng Geol 11229ndash42

Casu F Manzo M Lanari R 2006a A quantitative assessment of the SBAS algorithmperformance for surface deformation retrieval from DInSAR data Remote Sens Environ102195ndash210

Casu F Manzo M Lanari R 2006b Performance analysis of the SBAS algorithm for surfacedeformation retrieval Fringe 2005 Workshop Frascati Italy p 1ndash6

16 OIdreesOI Mohammed et al

Geo

cart

o In

tern

atio

nal

Crosetto M Crippa B 2005 State of the art of land deformation monitoring using differentialSAR interferometry ISPRS Hannover Workshop 2005 High Resolution Earth Imaging forGeospatial Information 2005 May 17ndash20 Hannover

Crosetto M Pasquali P 2008 DSM generation and deformation measurement from SAR dataISPRS J Photogramm Remote Sens 2157ndash167

ESA 2012 ESA Observing the Earth [cited 2013 Jan 18] Available from httpwwwesaintOur_ActivitiesObserving_the_EarthGMESSentinel-3

Fan J Zhao H Tu P Wang Y Guo X Ge D Liu G 2010 CRInSAR for landslide deformationmonitoring a case in threegorge area IEEE IGARSS International Geoscience and RemoteSensing Symposium Honolulu HI p 3956ndash3959

Fernaacutendez J Arjona A Prieto JF Santoyo MA Seco A Monells D Pallero JLG Prieto E LuzoacutenF Mallorquiacute JJ 2009 Application of CPT an advanced DInSAR technique to study surfacedisplacement near Itoiz dam Navarra Spain 95th Journeacutees Luxembourgeoises de Geacuteodyna-mique p 2ndash6

Ferretti A Prati C Rocca F 2000 Nonlinear subsidence rate estimation using permanent scatter-ers in differential SAR interferometry IEEE Trans Geosci Remote Sens 382202ndash2212

Ferretti A Prati A Rocca F Casagli N Farina P Young B 2005 Permanent scatterers technol-ogy a powerful state of the art tool for historic and future monitoring of landslides and otherterrain instability phenomena In Hungr O Fell R Couture R Eberhardt E editors Landsliderisk management Proceedings of the International Conference on landslide risk managementVancouver

Furuya M Mueller K Wahr J 2007 Active salt tectonics in the Needles District Canyonlands(Utah) as detected by interferometric synthetic aperture radar and point target analysis 1992ndash2002 J Geophys Res 1121ndash18

Gini F Lombardini F 2005 Multibaseline cross-track SAR interferometry a signal processingperspective IEEE AampE Syst Mag 2071ndash93

Herrera G Tomaacutes R Lopez-Sanchez JM Delgado J Vicente F Mulas J Cooksley G SanchezeM Duroe J Arnaude A et al 2009 Validation and comparison of advanced differential inter-ferometry techniques Murcia metropolitan area case study ISPRS J Photogramm RemoteSens 64501ndash512

JAXA (2012) Advanced Land Observing Satellite-2 (ALOS-2) Japan Aerospace ExplorationAgency [Internet] [cited 2013 Jan 18] Available from httpwwwjaxajpprojectssatalos2index_ehtml

Domiacutenguez J Romero R Carrasco D Martiacutenez A Mallorquiacute JJ Blanco P Navarrete D 2005Advanced DInSAR based on coherent pixels development and results using cpt techniqueFringe 2005 Workshop Frascati Italy p 1ndash6

Kampes B Adam N 2005 The STUN algorithm for persistent scatterer interferometry FringeATSR Workshop 2005 Advances in SAR Interferometry from ENVISAT and ERS missions2005 28 Novndash2 Dec Frascati Italy

Kampes B Adam N 2006 The STUN algorithm for persistent scatterer interferometry In Lacos-te H editor Fringe 2005 Workshop Frascat ESA Publications Division ESTEC p 1ndash28

Kampes BM Hanssen RF 2004 Ambiguity resolution for permanent scatterer interferometryIEEE Trans Geosci Remote Sens 422446ndash2453

Kenyi LW Kaufmann V 2003 Estimation of rock glacier surface deformation using SAR inter-ferometry data IEEE Trans Geosci Remote Sens 411512ndash1515

Lanari R Casu F Manzo M Lundgren P 2007 Application of the SBAS-DInSAR technique tofault creep a case study of the Hayward fault California Remote Sens Environ 10920ndash28

Lanari R Casu F Manzo M Zeni G Berardino P Manunta M Pepe A 2007 An overview ofthe small baseline subset algorithm a DInSAR technique for surface deformation analysisPure Appl Geophys 164637ndash661

Lauknes TR Dehls J Larsen Y Hoslashgda KA Weydahl DJ 2005 A comparison of SBAS and PSERS INSAR for subsidence monitoring in Oslo Norway Fringe ATSR Workshop 2005Advances in SAR Interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy p 1ndash8

Lee I Chang H-C Ge L 2005 GPS campaigns for validation of InSAR derived DEMs J GlobalPositioning Syst 482ndash87

Lu L Liao M 2008 Subsidence measurement with PS-INSAR techniques in Shanghai Urban IntArch Photogramm Remote Sens Spatial Inf Sci XXXVII173ndash178

Geocarto International 17

Geo

cart

o In

tern

atio

nal

Crosetto M Crippa B 2005 State of the art of land deformation monitoring using differentialSAR interferometry ISPRS Hannover Workshop 2005 High Resolution Earth Imaging forGeospatial Information 2005 May 17ndash20 Hannover

Crosetto M Pasquali P 2008 DSM generation and deformation measurement from SAR dataISPRS J Photogramm Remote Sens 2157ndash167

ESA 2012 ESA Observing the Earth [cited 2013 Jan 18] Available from httpwwwesaintOur_ActivitiesObserving_the_EarthGMESSentinel-3

Fan J Zhao H Tu P Wang Y Guo X Ge D Liu G 2010 CRInSAR for landslide deformationmonitoring a case in threegorge area IEEE IGARSS International Geoscience and RemoteSensing Symposium Honolulu HI p 3956ndash3959

Fernaacutendez J Arjona A Prieto JF Santoyo MA Seco A Monells D Pallero JLG Prieto E LuzoacutenF Mallorquiacute JJ 2009 Application of CPT an advanced DInSAR technique to study surfacedisplacement near Itoiz dam Navarra Spain 95th Journeacutees Luxembourgeoises de Geacuteodyna-mique p 2ndash6

Ferretti A Prati C Rocca F 2000 Nonlinear subsidence rate estimation using permanent scatter-ers in differential SAR interferometry IEEE Trans Geosci Remote Sens 382202ndash2212

Ferretti A Prati A Rocca F Casagli N Farina P Young B 2005 Permanent scatterers technol-ogy a powerful state of the art tool for historic and future monitoring of landslides and otherterrain instability phenomena In Hungr O Fell R Couture R Eberhardt E editors Landsliderisk management Proceedings of the International Conference on landslide risk managementVancouver

Furuya M Mueller K Wahr J 2007 Active salt tectonics in the Needles District Canyonlands(Utah) as detected by interferometric synthetic aperture radar and point target analysis 1992ndash2002 J Geophys Res 1121ndash18

Gini F Lombardini F 2005 Multibaseline cross-track SAR interferometry a signal processingperspective IEEE AampE Syst Mag 2071ndash93

Herrera G Tomaacutes R Lopez-Sanchez JM Delgado J Vicente F Mulas J Cooksley G SanchezeM Duroe J Arnaude A et al 2009 Validation and comparison of advanced differential inter-ferometry techniques Murcia metropolitan area case study ISPRS J Photogramm RemoteSens 64501ndash512

JAXA (2012) Advanced Land Observing Satellite-2 (ALOS-2) Japan Aerospace ExplorationAgency [Internet] [cited 2013 Jan 18] Available from httpwwwjaxajpprojectssatalos2index_ehtml

Domiacutenguez J Romero R Carrasco D Martiacutenez A Mallorquiacute JJ Blanco P Navarrete D 2005Advanced DInSAR based on coherent pixels development and results using cpt techniqueFringe 2005 Workshop Frascati Italy p 1ndash6

Kampes B Adam N 2005 The STUN algorithm for persistent scatterer interferometry FringeATSR Workshop 2005 Advances in SAR Interferometry from ENVISAT and ERS missions2005 28 Novndash2 Dec Frascati Italy

Kampes B Adam N 2006 The STUN algorithm for persistent scatterer interferometry In Lacos-te H editor Fringe 2005 Workshop Frascat ESA Publications Division ESTEC p 1ndash28

Kampes BM Hanssen RF 2004 Ambiguity resolution for permanent scatterer interferometryIEEE Trans Geosci Remote Sens 422446ndash2453

Kenyi LW Kaufmann V 2003 Estimation of rock glacier surface deformation using SAR inter-ferometry data IEEE Trans Geosci Remote Sens 411512ndash1515

Lanari R Casu F Manzo M Lundgren P 2007 Application of the SBAS-DInSAR technique tofault creep a case study of the Hayward fault California Remote Sens Environ 10920ndash28

Lanari R Casu F Manzo M Zeni G Berardino P Manunta M Pepe A 2007 An overview ofthe small baseline subset algorithm a DInSAR technique for surface deformation analysisPure Appl Geophys 164637ndash661

Lauknes TR Dehls J Larsen Y Hoslashgda KA Weydahl DJ 2005 A comparison of SBAS and PSERS INSAR for subsidence monitoring in Oslo Norway Fringe ATSR Workshop 2005Advances in SAR Interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy p 1ndash8

Lee I Chang H-C Ge L 2005 GPS campaigns for validation of InSAR derived DEMs J GlobalPositioning Syst 482ndash87

Lu L Liao M 2008 Subsidence measurement with PS-INSAR techniques in Shanghai Urban IntArch Photogramm Remote Sens Spatial Inf Sci XXXVII173ndash178

Geocarto International 17

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tern

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nal

Marghany M 2011 Three-dimensional visualisation of coastal geomorphology using fuzzyB-spline of dinsar technique Int J Phys Sci 66967ndash6971

Meisina C Zucca F Fossati D Ceriani M Allievi J 2006 Ground deformation monitoring byusing the permanent scatterers technique the example of the Oltrepo Pavese (LombardiaItaly) Eng Geol 88240ndash259

Michele C Erlinda B Javier D Josep C Alain A 2008 Generation of advanced ERS and envi-sat interferometric SAR products using the stable point network technique Photogramm EngRemote Sens 74443ndash450

Mora O Mallorqui JJ Broquetas A 2003 Linear and nonlinear terrain deformation maps from areduced set of interferometric SAR images IEEE Trans Geosci Remote Sens 412243ndash2253

Mouginot J Scheuchl B Rignot E 2012 Mapping of ice motion in Antarctica using synthetic-aperture radar data Remote Sens 42753ndash2767

Ng AH (2010) Advanced Satellite Radar Interferometry for Ground Surface Displacement Detec-tion School of Surveying amp Information System Sydney University of New South Wales(Unpublished)

Qi-huan H Xiu-feng H 2008 Surface deformation investigated with sbas-dinsar approach basedon prior knowledge Int Arch Photogramm Remote Sens Spatial Inf Sci XXXVII99ndash104

Seigmund R Bao M Lehner S Mayerle R 2004 First demonstration of surface currents imagedIEEE Trans Geosci Remote Sens 42511ndash519

Romero R Carrasco D Blanco P 2005 Advanced DINSAR based on coherent pixels develop-ment and results using CPT technique In Agency ES editor Fringe ATSR workshop 2005advances in SAR interferometry from ENVISAT and ERS missions 2005 28 Novndash2 DecFrascati Italy Vol 2005 p 1ndash22

Rosen PA Hensley S Joughin IR Li FK Madsen SN Rodriacuteguez E Goldstein RM 2000 Syn-thetic aperture radar interferometry IEEE Proc 88333ndash382

Tarikhi P 2010 Radar DEM generation achievements and benefits Wordpresscom [cited 2012Dec 6] Available from httpparviztarikhiwordpresscom

Tarikhi P 2011 NovemberDecember InSAR New ganeration Position Magazine AustraliaIssue 50 p 42ndash44

Tarikhi P 2012 Liqui-InSAR SAR Interferometry for aquatic bodies Wordpresscom Availablefrom httpparviztarikhiwordpresscom

Wang Y Ge D Hu Q Guo X 2008 Surface subsidence monitoring with coherent point targetSAR interferometry IEEE IGARSS International Geoscience and Remote Sensing Sympo-sium 2008 Jul 6ndash11 Boston MA p 1205ndash1208

Wegmuumlller U Santoro M Werner C Strozzi T Wiesmann A Lengert W 2009 DEM generationusing ERSndashENVISAT interferometry J Appl Geophys 6951ndash58

Werner C Wegmuumlller U Strozzi T Wiesmann A 2003 Interferometric point target analysis fordeformation mapping IEEE IGARSS International Geoscience and Remote Sensing Sympo-sium 2003 Jul 21ndash25 Toulouse France p 3ndash5

Werner C Strozzi T Wiesmann A 2005 ERSndashASAR integration in the interferometric point tar-get analysis Fringe ATSR Workshop 2005 Advances in SAR Interferometry from ENVISATand ERS missions 2005 28 Novndash2 Dec Frascati Italy p 1ndash6

18 OIdreesOI Mohammed et al

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