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Spectral angle mapper and object-based classification combined withhyperspectral remote sensing imageryfor obtaining land use/cover mapping ina Mediterranean regionGeorge P. Petropoulos a , Krishna Prasad Vadrevu b & CharitonKalaitzidis ca Institute of Geography & Earth Sciences, University ofAberystwyth, Aberystwyth, SY23 2EJ, UKb Department of Geographical Sciences, University of Maryland,Boston, USAc Mediterranean Agronomic Institute of Chania, Chania, Crete,Greece
Available online: 27 Feb 2012
To cite this article: George P. Petropoulos, Krishna Prasad Vadrevu & Chariton Kalaitzidis (2012):Spectral angle mapper and object-based classification combined with hyperspectral remote sensingimagery for obtaining land use/cover mapping in a Mediterranean region, Geocarto International,DOI:10.1080/10106049.2012.668950
To link to this article: http://dx.doi.org/10.1080/10106049.2012.668950
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Spectral angle mapper and object-based classification combined with
hyperspectral remote sensing imagery for obtaining land use/cover
mapping in a Mediterranean region
George P. Petropoulosa*, Krishna Prasad Vadrevub and Chariton Kalaitzidisc
aInstitute of Geography & Earth Sciences, University of Aberystwyth, Aberystwyth SY23 2EJ,UK; bDepartment of Geographical Sciences, University of Maryland, Boston, USA;
cMediterranean Agronomic Institute of Chania, Chania, Crete, Greece
(Received 11 December 2011; final version received 20 February 2012)
In this study, we test the potential of two different classification algorithms,namely the spectral angle mapper (SAM) and object-based classifier formapping the land use/cover characteristics using a Hyperion imagery. Wechose a study region that represents a typical Mediterranean setting in terms oflandscape structure, composition and heterogeneous land cover classes.Accuracy assessment of the land cover classes was performed based on theerror matrix statistics. Validation points were derived from visual interpretationof multispectral high resolution QuickBird-2 satellite imagery. Results fromboth the classifiers yielded more than 70% classification accuracy. However, theobject-based classification clearly outperformed the SAM by 7.91% overallaccuracy (OA) and a relatively high kappa coefficient. Similar results wereobserved in the classification of the individual classes. Our results highlight thepotential of hyperspectral remote sensing data as well as object-basedclassification approach for mapping heterogeneous land use/cover in a typicalMediterranean setting.
Keywords: Hyperion; spectral angle mapper; object-based classification;hyperspectral; land cover/use mapping
1. Introduction
Multispectral remote sensing data has been widely used for land use/covermapping at diverse spatial scales (Cihlar 2000, Carrao et al. 2008). Even thoughfeature extraction in multiple spectral bands has potential, it has its ownlimitations. For example, spectrally similar features are difficult to distinguishwhen only a small number of broad spectral bands are used (Thenkabail et al.2000). This is particularly true for various vegetation types when they portraysimilar annual phenologies (Borak and Strahler 1999, Carrao et al. 2008). Theprogress in remote sensing systems and sensor technology over the recent years hasled to the launch of hyperspectral remote sensing systems. These systems are ableto record reflected energy from land surface objects in several narrow continuous
*Corresponding author. Email: [email protected]
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ISSN 1010-6049 print/ISSN 1752-0762 online
� 2012 Taylor & Francis
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spectral bands from the visible to the shortwave infrared (SWIR) parts of theelectromagnetic spectrum, acquiring vast amount of spectral information (Xu et al.2008). As a result, the new hyperspectral sensors are able to provide an enhancedlevel of spectral information recorded by selective channels of the sensor (Galvaoet al. 2005, Dalponte et al. 2009). The ability of hyperspectral sensors to betterdiscriminate ground-cover classes than traditional multispectral sensors has beendemonstrated by several investigators (Zhang and Ma 2009 and references therein). Nowadays, hyperspectral remote sensing imagery is regarded as one of the ofthe most significant Earth observation data sources (Du et al. 2010) and is beingused for various applications including land cover classification and changedetection (Li et al. 2010).
One of the popular hyperspectral sensors is Hyperion. It is the first spacebornehyperspectral sensor launched in 2000 under NASA’s New Millennium Programonboard the Earth Observer-1 (EO-1) satellite platform. This sensor has the orbitalcharacteristics of Landsat ETMþ multispectral sensor but is 1 min behind,acquiring spectral information in 242 spectral bands and at a spatial resolution of30 m. Hyperion has two spectrometers, one in the visible and near infrared (VNIR)(bands 8–57, region 427–925 nm) and one in the SWIR region (bands 77–224,region 912–2395 nm). The swath width of Hyperion is 7.6 km across-track, andapproximately 53.6 km or 80.4 km along-track. A detailed description of theHyperion technical specifications is available in Folkman et al. (2001) and Ungaret al. (2003). The availability of Hyperion hyperspectral imagery has opened upnew opportunities to the remote sensing community and is widely used for severalapplications.
Over the last few decades, significant improvements in classification algorithmstook place (Mathur and Foody 2008, Lu and Weng 2007). Two importantclassification approaches include the pixel-based and the object-based classification.Pixel-based techniques employ the reflective characteristics of the land surfaceitems and their spectral signatures in order to perform classification by assigningpixels to land cover classes. Variations of such approaches include soft classifiers,sub-pixel classifiers and spectral un-mixing techniques. Generally, widely usedpixel-based classifiers such as the maximum likelihood (ML, Harris 1998) orartificial neural networks (ANNs) are able to often produce satisfactoryclassification results even in complex feature spaces using a small set of trainingsamples. More recently, another pixel-based classification technique that gainedpopularity is spectral angle mapper (SAM). It is a supervised classificationtechnique based on the computation of spectral angle similarity between areference source and the target spectra. The popularity of SAM is due to itssimplicity and rapid implementation for the examination of the spectral similarityof image spectra to reference spectra. It is also a very powerful classificationmethod because it suppresses the influence of shading effects to accentuate thetarget reflectance characteristics (De Carvalho and Meneses 2000). Despite thepotentiality of pixel-based methods, some of these techniques require makingassumptions regarding the probability distribution of the training datasets (e.g. inML), which might not always coincide to reality. Other classifiers such as ANN’smay require significant amount of effort in terms of calibration and fine tuning ofneural nets, before obtaining a satisfactory level of classification accuracy. It is alsogenerally argued that such classification approaches do not make use of the spatialconcept of the imagery, such as textural or contextual information present in aremotely sensed imagery (Yan et al. 2006).
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In contrast to pixel-based methods, object-based classification presents anotheroption for image classification. Object based methods were introduced in the early1970s (de Kok et al. 1999). They are based on the concept that informationnecessary to interpret an image is not represented in single pixels, but inmeaningful image objects. In this approach, knowledge-based membershipfunctions are developed defining a set of rules for classifying a feature (i.e. agroup of pixels), in contrast to pixel-based classification methods which are basedon applying a single decision-rule on a pixel by pixel basis (Walsh et al. 2008). Thefirst step in object-based classification is image segmentation based on whichremote sensing imagery is divided into regions where each is homogeneous and notwo adjustment regions are homogeneous (Pal and Pal 1993). In the next step, thesegmented image is used along with textural and contextual information as well asthe spectral information to produce a thematic map of land use/cover. Thischaracteristic of inclusion of spectral as well as spatial information of imageobjects, constitute the main advantage of the object-based classifiers. Mostimportantly, consideration of object attributes (e.g. shape, heterogeneity) results inthe reduction of the ‘salt and pepper’ effect and ‘edge’ seen usually in pixel-basedclassification (Fung et al. 2008). Blaschke (2010) recently provided an overview ofthe use of object-based image classification in remote sensing.
Hyperion imagery has been on the focus of land use/land cover classificationsince the early years of the sensor launch until today (e.g. Falcone and Gomez2005, Tiwari et al. 2010, White et al. 2010). Different algorithms, including SAMand object-based classification have been combined with Hyperion data in variousclassification applications (Eckert and Kneubuhler 2002, Thenkabail et al. 2004,Falcone and Gomez 2005, Galvao et al. 2005, Kutser et al. 2006, Pignatti et al.2009, Tiwari et al. 2010, Wang et al. 2010a). Nevertheless, to our knowledge, avery limited number of studies have so far been concerned in evaluating thecombined use of Hyperion imagery with different classification techniquescombined for obtaining land use/cover thematic maps, even so in Mediterraneanlocations. In this context, the aim of our study is to compare the potential of thecombined use of Hyperion data with SAM and object-based classificationalgorithms for land use/cover characterization in a typical Mediterranean setting.We also used very high resolution Quickbird-2 imagery to obtain ground truthinformation for validation in conjunction with the Hyperion data.
2. Study site
The study region located in the island of Crete in Greece, covers approximately230 km2, extending from 248040 to 248120 East, and from 358220 to 358360 North(Figure 1). The area is representative of a typical Mediterranean setting in terms oflandscape structure and composition. The terrain of the area is variable with thealtitude range of 0–800 m above sea level. The climate is typical Mediterraneancharacterized by hot and dry summers (April to September) and relatively mild andrainy winters. Geologically, the area is composed mainly by limestone anddolomites and by shallow carcalic lithosols soils. Vegetation varies based on theelevation and topography. Agriculture is the dominant land cover categoryinterspersed with vineyards, fruit trees and olive groves. The landscape is alsocovered with natural sclerophyllous vegetation and grasslands. Also, a large part ofthe area is covered by sparse vegetation of low height, alternating with bare rocks.Urban areas are mostly scattered.
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3. Datasets
The Hyperion image used in the study was acquired on acquired on 17 March 2006from the United States Geological Survey (USGS) archive (Figure 2, left). Theimagery was received as a full long scene (185-km strip) in geotiff format and at 1(L1GST) processing level, meaning that it was radiometrically corrected, geome-trically resampled, and registered to a geographic map projection with elevationcorrection applied (USGS 2008). In addition to the Hyperion imagery, a very highspatial resolution (0.6160.61 m) panchromatic satellite imagery from QuickBird-2sensor was acquired corresponding to 9 February 2006 from the Google Earth(image Catalog ID: 1010010004CC8801) (Figure 2, right).
4. Methods
Land use/cover classification was performed by implementing the SAM and object-based image classifiers to the Hyperion imagery. The pre- and post-processing stepsfollowed are summarised in Figure 3 and described below.
4.1. Data pre-processing
We first performed a linear interpolation of all the Hyperion sensor detectors on apixel by pixel basis, spectrum by spectrum and band by band to a common set ofwavelengths. This step was performed in ENVI software platform (v 4.7, ITT VisualSolutions) using the ‘Hyperion_tools.sav’ toolkit. The result was a 242-band image
Figure 1. Our study site location in the island of Crete in Greece, highlighted in theorthogonal box.
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with wavelengths representing the new common set of band centres and averagedfull-width at half-maximum values for each band. Subsequently, the non-calibratedHyperion bands (namely bands 1–7; 58–76; 225–242) were removed (USGS 2008).Hyperion bands sensitive to water absorption (i.e. bands 120–132, 165–182, 185–187,221–224) were also removed in order to reduce the influence of atmosphericscattering and water vapour absorption caused by well-mixed gases to the acquireddata. At this step, bands 77 and 78 were also eliminated from any further analysis asthese were characterized by a low signal to noise value, and overlap with bands 56and band 57, respectively.
Hyperion spectral bands were first visually interpreted. Vertical striping in theimages caused due to differences in gain and offset of different detectors wereidentified (Beck 2003), and were then manually removed (bands 8–9, 56–57, 79–80and 218–220). Subsequently, for each of the remaining spectral bands, the digitalnumber (DN) values were converted to at-sensor radiances. This was achieved bydividing the pixel’s DN by a constant value, which was 40 for the visible and near-infrared and 80 for the short-wave infrared (USGS 2008). As we used single image,no atmospheric corrections were performed as in Datt et al. (2003) and (Pengra et al.2007). Further, as the Hyperion imagery was acquired as terrain-corrected, nofurther correction for topographic effects deemed necessary.
As a last pre-processing step, a minimum noise fraction transformation (MNF;Lee et al. 1990) was applied. The MNF transform identifies and subsequentlysegregates from the signal received systematic noise presumed to arise from sensorand processing anomalies. MNF is regarded by many as an important pre-processingstep in Hyperion imagery analysis (Galvao et al. 2005, Pengra et al. 2007, Binal and
Figure 2. Hyperion (left) (FCC, R:42, B: 21, G: 11) and high resolution QuickBird–2 images(right) used in the present study. Acquisitions dates of the images were shown in the inset.
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Krishnayya 2009, Pignatti et al. 2009). MNF transformation was implemented inENVI (v. 4.7, ITT Visual Solutions) and it consists mainly of two steps. The firsttransformation decorrelates and rescales the noise in the data based on a noisecovariance matrix. The second step is a standard principal component transforma-tion that creates several new bands containing majority of the information (ENVIUser’s Guide 2008). All Hyperion bands from the last pre-processing step were usedas input to MNF (i.e. 149 bands in total). MNF transformation was appliedseparately for the VNIR and SWIR data as it allows better management of the noisedue to its different structure in the two datasets (Datt et al. 2003). The resultingMNF bands were analyzed for their spectral information content using eigen valueplots and individual MNF grey-scale bands. For the inverse MNF transformation,nine components in the VNIR and seven in the SWIR were finally used. Hyperion
Figure 3. Methodology followed for deriving the land use/cover maps for the study site usingthe Hyperion imagery with the different classifiers.
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final data set after the implementation of an inverse MNF consisted of 149 bands.These final bands after the above pre-processing steps were used in the present study.
4.2. Hyperion classification
4.2.1. Spectral angle mapper
A supervised classification using the SAM classifier was carried out using theHyperion image following three main steps. First, the classification key wasformulated which consisted of the classes summarized in Table 1. The classificationscheme was partly based on the inspection of the classes present at the QuickBird-2imagery and our own ground truth knowledge from previous work conducted in thestudy area. Second, approximately 65 training sites per class were collected from theHyperion imagery based on a stratified random sampling. The training sites werecarefully determined based on the homogenous nature of pixels with respect to tone,texture, association, etc., within a similar class. Their selection was guided by photo-interpretation of the QuickBird-2 very high spatial resolution imagery. Figure 4illustrates the average Hyperion spectra of the training sites collected for differentland cover classes. Using the training site data, we employed the SAM classifier usingthe ENVI image processing software.
SAM classifies the imagery based on the spectral similarity between imagespectra and the reference spectra (Kruse et al. 1993). The spectral similarity isdetermined by calculating the angle between reference spectra and satellite imagespectra treating them as vectors in an n-dimensional space where ‘n’ equal to thenumber of spectral bands of the sensor. Reference spectra can be generally takeneither from laboratory or field measurements or can be equally extracted directlyfrom the remote sensing imagery. In an n-dimensional multispectral space, a pixelvector has both magnitude (length) and an angle measured with respect to the axesthat defines the coordinate system of the space. In SAM, only the angularinformation is used for identifying pixel spectra, as the method is based on theassumption that an observed reflectance spectrum is a vector in a multidimensionalspace where the number of dimensions equals the number of spectral bands. Detaileddescription of the SAM algorithm is given in Kruse et al. (1993). The n-dimensionalspace for the SAM implementation was defined by the total number of Hyperionbands remained after the implementation of the last pre-processing step, namely the
Table 1. Classification key used in the present study.
Class name ID Class description
Sparsely vegetated areas 1 Open areas with little or no vegetation of low heightPermanent crops 2 Areas covered by different types of permanent
crops, mainly olive treesHeterogeneous agricultural
areas3 Cultivated fields of varied plantations,
land principally agricultureSchlerophyllous vegetation 4 Scrubland and/or herbaceous vegetation and
mixed associationsNatural grasslands 5 Areas covered mainly by low vegetation, mainly
different types of grassBare land 6 Urban fabric, discontinuous urban areas,
bare rocks, bare soilSea 7 Sea water
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MNF transformation. A single value of 0.3 radians was used as the maximumclassification threshold value for all classes. This threshold was selected after carefulevaluation of different threshold angle values ranging from 0.18 to 0.68. Selection ofthe parameters used in SAM implementation was based on performing a number oftrials of parameter combinations, using classification accuracy as a measure ofquality (e.g. Pal and Mather 2005, Lu and Weng 2007, Kuemmerle et al. 2009,Petropoulos et al. 2011).
4.2.2. Object-based classification
In the object-based classification, each classification task addresses a certain scale.The image information can be represented in different scales based on the averagesize of image objects and the same imagery can be segmented into smaller or largerobjects (Walsh et al. 2008). Object-based classification relies on the assessment ofspatially neighbouring groups of pixels with a certain degree of spectral similarity,rather than individual pixels. The process of identifying such groups of pixels havingsimilar characteristic called segmentation can produce variable number and size ofobjects depending on the thresholds of spectral similarity and compactness (De Koket al. 1999). The process of segmentation merges neighbouring pixels with similarspectral properties into individual objects. The shape parameter indicates theimportance of the shape of the object, over its spectral characteristics, whilethe compactness affects how compact the objects will be. The scale parameter definesthe degree of similarity threshold and dictates whether a pixel will participate in theformation of an object or not. A hierarchy of levels of segmentation ranging from afew large objects to many smaller objects can be obtained with each object belonging
Figure 4. Average spectra collected from the Hyperion sensor for each of the classes used inthe classification scheme developed in the present study.
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to a super-object at a higher level of segmentation and smaller objects at a lower levelof segmentation. The spectral characteristics of each object not only includes anaverage value for each band from the pixels participating in the object, but alsostatistical values such as minimum, maximum and standard deviation regarding eachband. In addition, the objects are described by shape, size, tone, texture,compactness and other characteristics describing the spatial features of the object(Bock et al. 2005). All of those variables can be used in the classification process toassist in the discrimination of the objects and their correct assignment to the landuse/cover classes. Further, each object ‘inherits’ the characteristics of the super-object it belongs to, and passes its own characteristics to the sub-objects (Benz et al.2004). The classes used in the key are structured in a tree-like class hierarchy, where,similarly to the super/sub-object relationship, sub-classes at the lower levels of thestructure inherit characteristics of the super-classes at higher levels of theclassification tree. While the inheritance hierarchy is used to subsequently separateclasses in the feature space, the groups’ hierarchy permits meaningful groupings ofthe resulting classes (Blaschke 2010).
In our study, the Hyperion image was segmented at five levels, using all 149bands with equal weighting, shape parameter of 0.1, compactness of 0.5 and scalefactors of 50, 10, 5, 3 and 2 for each level. As in SAM, object-based parameterizationwas performed based on ‘trial and error’ approach using classification accuracy as ameasure. The shape and compactness parameters, used in image segmentation wereselected based on visual interpretation. Before applying the classifier, the ‘Sea’ and‘Bare Land’ classes were identified using brightness and band 24 (red region). Thelargest homogenous areas belonging to those classes were identified at the top levelof segmentation (scale factor of 50). At the scale factor of 50, band 24 of Hyperiondiscriminated between Sea, Bare Land and other classes for certain segments. Thechoice of threshold was made based on visual interpretation. Assigning those objectsto the respective classes at such a coarse scale, removed the possibility of thembecoming misclassified. At each subsequent level, class assignment from the levelabove was inherited and additional smaller polygons belonging to those classes wereclassified using brightness and band 24 values. Samples for all the classes (with theexception of the ‘Sea’ class) were selected from the fourth level (scale factor 3) andwere used for the nearest neighbour classifier. The algorithm was applied on thethree bottom layers (scale factors 5, 3 and 2). Objects in the fourth level (scale factor3) classified as ‘permanent crops’ were reclassified to ‘heterogeneous agriculture’, ifthe super-object was classified as ‘heterogeneous agriculture’. In addition, objectsclassified as ‘sparse vegetation’ were reclassified to ‘sclerophyllous’, if more than50% of the area covered by the sub-objects were classified as ‘Sclerophyllous’. Theclassification map which was derived following the above approach was subse-quently exported into ENVI compatible format for accuracy assessment.
4.3. Classification accuracy assessment
Accuracy assessment was based on the computation of the overall accuracy (OA),user’s accuracy (UA), producer’s accuracy (PA) and the Kappa (Kc) statistic(Congalton and Green 1999). The OA is the ratio of the number of validation pixelsthat have been correctly classified to the total number of validation pixels used for allclasses and is expressed as percentage (%). Kc is the proportion of correctly classifiedvalidation points after random agreements are removed and it expresses the extent to
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which the confusion matrix results are not obtained by chance or random. Thus, incomparison to OA, Kc indicates a more conservative estimation than a simplepercentage value. PA expresses the probability that the classifier has correctlylabelled an image pixel, whereas UA expresses the probability that a pixel belongs toa given class and the classifier has labelled the pixel correctly into the same givenclass. We selected approximately 20 validation points for each class on the Hyperionimagery guided primarily by the QuickBird-2 very high resolution satellite imagery(0.6160.61 m pixel size) acquired closely in time to Hyperion image. Further, thevalidation sites were selected away from the locations where the random points werecollected. This step ensured non-overlap of pixels between the training data andvalidation sites. Further, to ensure consistency, the same set of validation pointswere used in evaluating the classification accuracy of the thematic maps produced bythe SAM and object-based classifiers on the Hyperion imagery.
5. Results and discussion
Results obtained from the classification of Hyperion imagery using SAM andObject-based approach were shown in Figures 5 and 6 and the related statisticsderived from the computation of the error matrix are shown in Table 2. In general,both the classifiers yielded more than seventy percent accuracy. However, Object-
Figure 5. The Hyperion classification using the spectral angle mapper (SAM) classifier.
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based classification outperformed SAM in terms of OA by 7.91% with a relativelyhigh (0.163) Kappa accuracy. As regards the individual class accuracies from SAM,the UA and PA ranged between 53–100% and 4–100%, whereas for object-basedclassification UA and PA ranged from 62 to 100% and 47 to 100% (Table 2).
Table 2. Classification accuracy assessment results obtained for our study site.
Object-basedSpectral angel mapper
(angle¼ 0.3)
Land cover classesProducer’s
accuracy (%)User’s
accuracy (%)Producer’s
accuracy (%)User’s
accuracy (%)
Sea 100.00 100.00 100.00 100.00Natural grasslands 100.00 94.74 94.44 73.91Sparsely vegetated areas 80.00 62.50 60.00 55.56Permanent crops 50.00 62.50 70.00 53.85Sclerophyllous vegetation 47.83 73.33 43.48 55.56Heterogeneous agricultural areas 81.25 76.47 43.75 63.64Bare land 100.00 96.30 100.00 100.00Overall accuracy (%) 82.01 74.10Kappa coefficient 0.787 0.695
Figure 6. The Hyperion object-based classification.
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Interestingly, for both the classification techniques, the individual classes with thehighest UA and PA were the ‘sea’ and ‘bare land’. For the SAM classified classes of‘sparse vegetation’, ‘permanent crops’ and ‘sclerophyllous vegetation’ the UA wasvery close (53–55%). This lower classification accuracy from SAM for these classes ismainly attributed to their close spectral similarity in the Hyperion imagery (Figure4), which in conjunction with the Hyperion 30 m resolution, hindered classdiscrimination mainly due to spectral mixing. Spectral mixing can becomeproblematic as the sensor spatial resolution decreases and as the Earth’s surfaceheterogeneity increases (Xu et al. 2008), which is often the case for example inMediterranean landscapes. Especially in the highly fragmented Mediterraneanregions, the effect of mixed pixels can be quite pronounced and use of spectralsignature alone for classification may not yield good results, as evidenced in our case.Our validation efforts in this study were based on integrating Hyperion withQuickBird-2 imagery which assisted in selecting highly homogenous ground controlpoints having similar spectral similarity, however, there is a scope for improvement.
A variety of scale, compactness and shape/colour parameters were evaluatedduring the segmentation process of the image. The aim of the segmentation process isthe production of ‘meaningful’ objects, hence it was imperative to reduce the scalefactor of the segmentation to an extent where no more than one land cover type wasincluded in each object (Burnett and Blaschke 2003, Dr�agut et al. 2009). Theclassification key employed in this study consists of land cover types occupying largeareas and hence, it was not necessary to resolve to a detailed segmentation.Furthermore, low scale factor values results in numerous small objects, leading tohigh within-class spectral variability and an increase to the risk of misclassification ofthose objects. As a general rule, over-segmentation is acceptable, while under-segmentation is not (Weidner 2010). The optimal combination produced asegmented image where the population of objects was kept at a minimum, whileensuring that no object suffered from land cover type heterogeneity. The finalselection of those parameters was based on visual assessment of the homogeneity ofthe objects and the initial results of the classifications performed following thevarious segmentations.
In contrast to the SAM, the object-based classification yielded relatively higherUA for different classes. Results suggest that the segmentation process combinedwith the contextual information derived from image ‘objects’, substantially helps inachieving higher accuracy. Specifically, aggregation of the pixels into objectsobtained through the image segmentation assist in reducing the pixels variability andthus the ‘salt and pepper effect’, generally found in pixel-based classifiers includingthe SAM (Yan et al. 2006). In addition, the higher classification accuracy obtainedfrom the object oriented method is also attributed to the inclusion of additionalinformation on image data, such as the object size, object complexity, texture andspectral difference to neighbouring objects, in addition to spectral information (Benzet al. 2004, Fung et al. 2008). Further, the objects created can be easily converted tovector files for further analysis in a Geographical Information Systems (GIS)environment (Bock et al. 2005, Blumberg and Zhu 2007). However, it should benoted that in comparison to SAM, object-based classification requires more expertiseand time in its implementation, being however computationally marginally moreexpensive.
The results obtained in the study compare favourably with the results obtainedelsewhere using the Hyperion imagery and either SAM or object-based classification
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(Falcone and Gomez 2005, Tiwari et al. 2010). For example, Walsh et al. (2008)compared the performances of three classifiers, namely of SAM, spectral unmixingand object-based classification combined with Hyperion imagery for mappinginvasive plant species in Ecuador and reported that the object-based classificationoutperformed the other two techniques. Wang et al. (2010b) applied an object-basedclassification to a Hyperion imagery acquired for a test region in China and showedan OA ranging from 72 to 88%, depending on the number of classes delineated.Similar results highlighting the potential of object-based classification approachescan be found in Blumberg and Zhu (2007), Zhou and Troy (2009), Zhang and Huang(2010) and Wang et al. (2010b).
In summary, our results suggest that low cost Hyperion hyperspectral satelliteimagery combined with object-based classification can aid in mapping different landuse/cover classes with high accuracy over the Mediterranean regions. However, froman operational point of view, in particular mapping land cover characteristics at aglobal scale using object-based approach may face some limitations. For example,the calibrating the rule-set scheme by adjusting the threshold values for theindividual class features accounting for possible atmospheric contamination, andspectral difference of the same feature in different sub-regions can be difficult. Incontrast, if using SAM, further work should be directed towards the inclusion ofadditional spectral information content (e.g. topographic information, radiometricindices, textural information, Principal Component Analysis (PCA), etc.) to aid andenhance the overall classification performance by this technique. Last but not least,the potential of Hyperion imagery needs to be explored for land use/cover mappingand monitoring studies in diverse regions, apart from Mediterranean environments.
6. Conclusions
The potential of remote sensing data for land use/cover delineation using traditionalalgorithms has been widely explored by the earlier researchers in the Mediterraneanregion, in contrast to novel algorithms such as SAM and object-based methods. As acase study, we tested the above algorithms in an highly heterogeneous landscape,Crete, Greece for land use/cover delineation using Hyperion hyperspectral imagery.Although, both the classifiers yielded more than 70% accuracy, object-basedapproach outperformed the SAM in both the OA and kappa coefficient. We notethat SAM classifier in comparison to other pixel-based non parametric classifiers ismuch easier to implement and also has the ability to reduce possible shading effects.On the contrary, object-based classification appeared most useful in describing thespatial distribution and the cover density of each land cover category in the studiedregion. Nonetheless, the object-based technique was found to be more demanding inits implementation in terms of user expertise. For both techniques, an importantlimitation seems to be their inability to operate on a sub-pixel level, especially overhighly heterogeneous surfaces and when coarse spatial resolution remote sensingimagery is used for classification. Thus, there is a strong need for new algorithmsthat can take into account both the sub-pixel heterogeneity as well as contextualinformation for land use/cover delineation.
Acknowledgements
The authors wish to thank the United States Geological Survey (USGS) and Google Earth forthe datasets. The authors express their gratitude to the anonymous reviewers for their
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constructive comments and suggestions. Dr. Petropoulos thanks INFOCOSMOS E.E. (http://www.infocosmos.eu) for organizational support during his participation in this work.
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