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BACHELOR THESIS TOPIC SAR INTERFEROMETRY IN MOUNTAINOUS AREAS
Context Synthetic Aperture Radar Interferometry is a well established technique allowing to retrieve information about surface model or deformation. The main limitation of this technique is the validity of the domain of application. Indeed, application of interferometry depends on many parameters and terrain slopes, making it difficult to apply it on very mountainous areas.
Fortunately, thanks to the so-called range spectral filtering, it is possible to overcome this problematic.
!Thesis objective The aim of the bachelor thesis is to investigate the potential of a full topographic dependent range spectral filter over different scenario: subduction, earthquake, volcano, and to quantify the observed improvement.
!Expected profile Applicant should have an interest in radar remote sensing. Knowledge in synthetic aperture radar would be a plus. Programming skills are required (C++).
!Contact The master thesis student will be working at the Computer Vision and Remote Sensing Department under the supervision of Dr. Stéphane Guillaso
Interested candidates are invited to contact Stéphane Guillaso ([email protected]) for more information or to submit the candidacy.
TOPOGRAPHIC RANGE SPECTRAL FILTERING
InSAR measurement of! interseismic strain in areas of low coherence: example across the Haiyuan fault (Gansu, China) using a local InSAR adaptive range filter.
Stéphane Guillaso (1), Marie-Pierre Doin (1), Cécile Lasserre (1), Olivier Cavalié (2), Sun Jianbao (3), Gilles Peltzer (4)
(1) Laboratoire de Géologie, ENS, Paris, France (2) LGIT, Université Joseph Fourier, Grenoble, France (3) Chinese Academy of Sciences, Benjing, China (4) University of California, Los Angeles, United States
INTRODUCTION
Raw
DataRaw
DataRaw
DataRaw
Data
dop = median{dopn}N1
doppler calculation
�az =L
2(1� L2vel |max{dop0n · prfn}N
1 �min{dop0n · prfn}N1 |)
azimuth resolution
DEM
Correct DEM
Select
Doppler
Raw
DataRaw
DataRaw
DataSLCs
Select
Master
Coregistration
SLC
(1)
SLC
(N)
SLC
(2)
Select Interferograms
(Fig. 3)
Interferogram
generation
(See Fig. 7)
DEM OK ?
Raw
DataRaw
DataRaw
DataInterferogram
Stacks
Velocity of fault
estimation
MasterSlave
(N-1)
Slave
(1)
Interferogram
generation
(See Fig. 7)
no
yes
ANALYSIS STRATEGY
- Simulated interferometric phase used to
coregistrate data in range
- Amplitude correlation used for azimuth
Same slant range geometry !!!
38º
36º
102º 104º
1927/23/05M8-8.3
1920/12/16M8.7
Tianjin shan Mibo shan
Gulang F.
Haiyuan F.
LLL
JQH
MMS
LHS HS
Gobi desert
Yellow
river
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I D A M
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OR
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S
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tlua
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r
SOUTH
CHINA
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HS
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T I B E T
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40°
35°
30°
25°
90° 100 °80° 110 °
Haiyuan fault
(a)
(b)
T104
Figure 1: Seismotectonic map of the Haiyuan fault system
This study concerns the measurement of the interseismic deformation
across the western Haiyuan fault (Fig. 1), one of the major strike slip
fault in China.
A previous InSAR study shows that along the two easternmost SAR
tracks, a steep velocity gradient has been observed across!the fault,
consistent with a left-lateral slip at a rate of 6.3±2 mm/yr below a small
apparent locking depth (<2 km), which may be indicative of transient
superficial creep or related to a weak fault zone [Cavalié et al. 2008].
The western track has not been studied yet as it covers a very high
mountainous area, which introduces strong geometrical decorrelation.
In this study, a topographic adaptive range spectral filter is proposed in
order to improve phase coherence over the mountainous study area.
measured spectrum of image 1
ground object spectrum
f̂g
f̂z
∆f∆f
f̂1
f̂2
measured spectrum of image 2
common part of spectra
θ1
θ2
altitude
range
near range middle range far range
Using ROI_PAC Using our topographic range spectral filtering
range range
azim
uth
azim
uth
469 - 0.47
259 - 0.18
059 - 0.18
160 - 0.08
029 - 1.71
473 - 1.33
160 - 2.48
025 - 3.53
116 - 1.04
279 - 0.56 235 - 2.01
Temporal baseline0 1 2 3
0
400
300
200
100
No
rma
l b
ase
line
CONCLUSION AND FUTURE WORK
Figure 4: Spectral shift principle in spectral domain
Figure 5: Range dependance of spectral common part
2003 2004 2005 2006 2007 2008
Temporal baseline [year]
-200
0
200
400
600
800
1000
1200
Norm
al baselin
e [m
]
B_N < 50m & B_T < 5y
B_N < 300m & B_T < 3y
B_N < 500m & B_T < 1.5y
Unwrapping
Orbit / Atmospheric Correction
Figure 3: Interferogram selection graph
Figure 2: Block diagram of the analysis strategy
n = n + 1
oversampling (x2)
DEM
estimate simulated topographic phase
�(x, n)
estimate local topographic phase slope
��(x, n) =⇥�(x, n)
⇥x
define common part of spectra
�f =rbw
rsf� ⇥�(x, n)
2�
range topographic spectral shift
u1,2(x, n) = u1,2(x, y)ei±�(x,n)2
range topographic spectral filtering
u1,2(x, n) = u1,2(x, n) � {�f(x, n) · sinc(�f(x, n))}
undersampling (x2)
interferogram generation
i(x, n) = u1(x, n)u�2(x, n)
u1,2(x, n)
Figure 7: Interferogram generation block diagram
frequency
am
plit
ud
e�f
frequency domain
range
am
plit
ud
e
spatial domain
�f
�f
range spectral shift
topographic range
spectral filtering
interferogram generation(variable
resolution)ra
ng
e
ran
ge
ran
ge
ran
ge
frequency frequency frequency frequency
Figure 6: topographic range spectral filtering principle
Figure 8: Comparison between standard ROI_PAC chain and our topographic range spectral filtering (473m normal baseline, 1.3 y temporal baseline)
Figure 9: Interferogram results the track 104 (ENVISAT)
Raw
interferogram
filtered
interferogram
unwrapped
filtered
interferogram
Residue (±!)
low pass
filtering
high pass
interferometric
phase
–
interferometric
phase DEM
correction
filtering
unwrapping
baseline
ratio
+
Unwrapped
raw
interferogram
–
Figure 10: DEM correction strategy Figure 11: DEM correction, test over small area
" The proposed topographic range spectral filtering in the spatial domain gives very promising results in order
to generate high coherence interferograms in mountainous areas.
" It also makes possible the generation of interferograms with large normal baselines.
" An improvement of the DEM, using a set of interferograms with small baselines, is needed.
" The next steps are to correct orbital errors and to remove atmospheric phase delays following Cavalié et al.
2008, to provide information about surface velocity, particularly in the western, mountainous part of the
track.
" Results will be combined with adjacent track (333) to the east (1/3 common part).
REFERENCESCavalié et al. "Measurement of interseismic strain across the Haiyuan fault (Gansu, China), by InSAR",
submitted to Earth Planet. Sci. Lett. 2008 (in revision)
Gattteli et al. "The wavenumber shift in SAR interferometry, " IEEE Trans. Geosci. Remote Sens., vol. 29, no. 5,
pp. 855-864, 1994
Kampes, "Radar Interferometry: Persistent Scatterer Technique", Remote Sensing and Digital Image Processing
vol 12, Springer, 2006
�total = �temporal�spatial�doppler�thermal
⇥⇤1� f
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T c
⇥⌅ ⇤1� f
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⇥⌅ ⇤1� f
�FDC
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⇥⌅�thermal
f(x) =�
x, for x � 11, for x > 1where
Before After