research article a method of spatial mapping and...

Hindawi Publishing CorporationThe Scientific World JournalVolume 2013 Article ID 192982 7 pageshttpdxdoiorg1011552013192982

Research ArticleA Method of Spatial Mapping and Reclassification forHigh-Spatial-Resolution Remote Sensing Image Classification

Guizhou Wang12 Jianbo Liu1 and Guojin He1

1 Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing 100094 China2University of Chinese Academy of Sciences Beijing 100049 China

Correspondence should be addressed to Guojin He gjheceodeaccn

Received 24 September 2013 Accepted 19 November 2013

Academic Editors Z Hou and R D J Romero-Troncoso

Copyright copy 2013 Guizhou Wang et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanismof spatial mapping and reclassification The proposed method includes four steps First the multispectral image is classified by atraditional pixel-based classificationmethod (support vectormachine) Second the panchromatic image is subdivided bywatershedsegmentation Third the pixel-based multispectral image classification result is mapped to the panchromatic segmentation resultbased on a spatial mappingmechanism and the area dominant principle During themapping process an area proportion thresholdis set and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold Finallyunclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm Experimentalresults show that the classification method for high-spatial-resolution remote sensing images based on the spatial mappingmechanism and reclassification strategy can make use of both panchromatic and multispectral information integrate the pixel-and object-based classification methods and improve classification accuracy

1 Introduction

With the development of improved sensors and power-ful computation technology high-spatial-resolution remotesensing data have been more easily acquired and widelyapplied [1] High-spatial-resolution remote sensing imagescontain more information and have increased the detail atwhich earth observations can be made The abundance ofinformation on one hand has promoted the applicationof remote sensing methods but on the other brings newtechnological challenges to the data analysis One challenge isthat traditional image classification technology can no longersatisfy the needs of high-spatial-resolution remote sensingimage classification

High-spatial-resolution remote sensing imagery such asSPOT-5 IKONOS and QuickBird has been used in manyfields in recent years [2] They have been applied for urbanplanning urban change detection tree canopy mapping

ecological environment monitor precision agriculture andso forth [3] The main difference between a high-spatial-resolution remote sensing image and a low- or medium-resolution remote sensing image is that the high-spatial-resolution image provides more useful information such asshape and texture Therefore the extraction of geographicalinformation from a high-spatial-resolution satellite image istopical [2]

The traditional method of classification for high-spatial-resolution images has been proven to have several draw-backs such as low classification accuracy the derivationof very limited spatial information and salt and peppereffects [2] Therefore novel and efficient analysis techniquesare needed for processing and analyzing of high-spatial-resolution remote sensing images Many studies have beendone on segmentation and classification of high-spatial-resolution remote sensing images [2ndash8] Tarabalka et al[4] presented a new spectral-spatial classification scheme

2 The Scientific World Journal

for hyperspectral images combining the pixel-based sup-port vector machine classification results and the water-shed segmentation regions together Bruzzone and Carlin[6] proposed a novel pixel-based system for the classifica-tion of high-spatial-resolution images The spatial contextinformation of each pixel extracted from multilevel seg-mentation images was used to obtain more accurate andreliable classification result Chen et al [2] introduced amodified object-oriented classification algorithm integrat-ing multicharacteristics of high-spatial-resolution remotesensing image Unsalan and Boyer [7] put forward a newclassification method of land development in high resolutionpanchromatic satellite images using straight line statisticsSalehi et al [8] developed a hierarchical rule-based object-based classification framework coupledwith height points forcomplex urban environment classification The rule set wasextracted from a training set of QuickBird image coupledwith a layer of height points In addition the morphologicalbased segmentation and classification was investigated in thework of Dalla Mura et al [5] and Pesaresi and Benediktsson[9] Although some improved algorithms can increase theclassification accuracy by making use of spectral and texturalinformation the classification results still cannot satisfy allactual needs Most of these methods were developed onfusion images or multispectral images The spatial relation-ship of high-spatial-resolution remote sensing image betweenpanchromatic and multispectral bands has not been fullyconsidered

This paper proposes a newhigh-spatial-resolution remotesensing image classification method based on a mechanismof spatial mapping and a strategy of reclassification Theanalysis makes use of both spectral and spatial informationfrom high-spatial-resolution remote sensing data The clas-sification framework uses a spatial mapping mechanism tofit the special data format and the content of high-spatial-resolution remote sensing imagesThis algorithm is not basedon a fusion image but rather on raw high-spatial-resolutionremote sensing data which makes full use of the spatialresolution relationship of panchromatic and multispectralimages

QuickBird and SPOT-5 satellite data were employed in aseries of experiments and comparative analyses Traditionalpixel-based SVM object-oriented SVM and the methodproposed in [4] (SVM + Majority Voting SVMMV) wereused for comparison to analyze the advantage of the pro-posed method Experimental results show that a classifica-tion method based on a spatial mapping mechanism andreclassification strategy for high-spatial-resolution remotesensing data can make full use of the information in bothpanchromatic and multispectral bands integrate the pixel-and object-based classification methods and improve theclassification accuracy

2 Methodology

This section details the support vector machine classificationmethod andwatershed image segmentation and outlines thespatial mapping mechanism and reclassification strategy A

Multispectral image Panchromatic image

Pixel-based classification Image segmentation

Mapping classification result from multispectral to panchromatic

Reclassify unclassified regions

Final panchromatic classification result

Figure 1 The flow chart of the proposed classification method

flow chart of the proposed classification method is shown inFigure 1

21 Support Vector Machine Classification The first stepin the proposed method is the pixel-based classificationof a multispectral image There are many possible imageclassification algorithms for remote sensing images Eachalgorithm has its unique advantages and weaknesses In thispaper we focus on the application of the spatial mappingmechanism and reclassification strategy for high-spatial-resolution remote sensing images In order to improve theoverall efficiency of approach and to simplify it a supportvector machine classifier is chosen Theoretically any pixel-based classification method can be applied in the proposedmethod

Support vector machine (SVM) is a supervised non-parametric statistical learning technique and widely usedin classification of remote sensing images [10] SVM iswell at solving nonlinear high dimensional and limitedtraining samples [8] In this paper LIBSVM library is usedto implement the SVM classification algorithm [11]

22 Watershed Image Segmentation Image segmentationis an important part of image interpretation especiallyfor high-spatial-resolution remote sensing images [12 13]High-spatial-resolution remote sensing images contain moreinformation of ground objects and show great diversity ofthem The purpose of segmentation is to divide an imageinto homogeneous regions Watershed transformation is apowerful mathematical morphology technique for imagesegmentation [4 14] A watershed algorithm is a good choicefor high-spatial-resolution remote sensing images because ofits fast segmentation speed [15]

In this paper a labeledwatershed segmentation algorithmwas used to segment the panchromatic imageMorphologicaloperators were used to characterize the gradient of the imagewhich enabled the watershed segmentation algorithm to labelareas within the image

The Scientific World Journal 3

23 Spatial Mapping Mechanism High-spatial-resolutionremote sensing data often contain two types of imagesthose with a single panchromatic band and those with fourmultispectral bands [16] For example QuickBird imageshave a panchromatic band with a resolution of 06m andfour multispectral bands with a resolution of 24m Apanchromatic image of high-spatial-resolution remote sens-ing data contains most of the spatial information whereas amultispectral image has most of the spectral information

In order to make full use of panchromatic and multi-spectral image information image fusion is widely used tointegrate the two The fusion algorithm merges the high-resolution panchromatic and low-resolution multispectralimagery to create an enhanced high-resolution multispec-tral image Then the fused image is used in subsequentapplications Note that the fused image is an estimationwhich may cause spectral distortion and affect the accuracyof classification results [16] The effect of fusion directlydetermines the subsequent application accuracy

The proposed classification framework uses a spatialmapping mechanism to make full use of spatial and spectralinformation in high-spatial-resolution remote sensing dataIn the presented method the raw high-spatial-resolutiondata instead of the fusion image was directly classified basedon a spatialmappingmechanism and reclassification strategyFor example Figure 2 shows the spatial mapping relationshipbetween panchromatic and multispectral images taking aresolution ratio of 1 4 One pixel in the multispectral imagecorresponds to sixteen pixels in the panchromatic image Forexample if a pixel has position (119909

119894 119910119895) in the panchromatic

image then 119894 = 1 2 119898 119895 = 1 2 119899 the correspondingposition of the pixel in the multispectral image is (1199091015840

119894 1199101015840119895)

where 1199091015840119894= Int(119909

1198944 + 04) and 1199101015840

119895= Int(119910

1198954 + 04) The

Int( ) function rounds a number to the nearest integerThe pixel-based multispectral classification result was

mapped to the panchromatic segmentation result basedon the spatial mapping mechanism and ldquoarea dominantrdquoprinciple When using the area dominant principle as amapping ruler the areal proportion of each class in eachregion is computed and a class label corresponding to themaximum area proportion is assigned to that region Figure 3shows the spatial mapping mechanism based on the areadominant principle On the left is the panchromatic imagewith the original class labels whereas spatial mapping isbased on the multispectral image pixel-based classificationresult On the right are the regions of the panchromaticimage segmentation After spatial mapping based on the areadominant principle region one is labeled ldquoardquo and region twois labeled ldquobrdquo

The area proportion of each class represents the regionrsquosmembership to every class Regions composed of only oneclass of pixels have a higher area proportion close to onefor this class and zero for other classes However regionscomposed of pixels belonging to several different classeshave a lower area proportion for every class The maximumarea proportion of each region reflects the ambiguousnessmapping from the pixel-based classification During themapping process an area proportion threshold (0 lt 119879 le 1)


Figure 2 The spatial mapping relationship between multispectraland panchromatic images taking a resolution rate of 1 4 forexample

is set The region is labeled as unclassified if the maximumarea proportion does not surpass the threshold The greaterthe threshold the greater the classified regions reliability Inthis paper the threshold was set to 06The regional propertyis considered very reliable if the maximum area proportionis greater than 06 Unclassified regions are reclassified in thenext step

24 Reclassification Strategy Unclassified regions in theimages will be reclassified based on spectral informationusing the minimum distance to mean (MDTM) algorithm[17]Theminimum distance classification uses a mean vectorfor each class and calculates the Euclidean distance fromeach unclassified region to the mean vector for each classAll unclassified regions were classified to the closest classIn this paper the classified regions through spatial mappingmechanism based on the area dominant principle were usedas training samples

The regional spectral vector is calculated by the meanspectral vector of pixels contained in each regionThe spectralvector of each pixel in a panchromatic image is obtained froma multispectral image by a spatial mapping mechanism

A MDTM classifier computes the Euclidian distance inspectral space between themean of every class in the trainingset and the region to be classified The Euclidian distancebetween the mean of a class and an unclassified region in the119899 dimensional spectral feature space is given as [18]

119863ED = (119899

sum119894=1

(119909119894minus 119862119894)2)

12

(1)

where 119899 is the dimensionality of data 119909119894is the mean spectral

value of the 119894th band of the unclassified region and 119862119894is

the mean spectral value of the 119894th band of one class Theunclassified region is then assigned to the class where 119863EDis minimal


a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

a

a

a

a

a

a

a

b

b

b

b

b

b

b

Original class label

(a)

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

2 2 2 2

1 1 1 1 2 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2

Segmentation regions

(b)

Figure 3 Mapping mechanism by the area dominant principle

(a) (b) (c)

(d) (e) (f)

RoadWaterArtificial grass

BuildingBare landPlastic track

VegetationShadowUnclassified

Figure 4 QuickBird images and classification results (a) is the fused pseudocolor synthetic image (b) is the primitive mapped classificationimage (c) shows the final classification result obtained by the proposed method and (d) is result of the pixel-based SVM The classificationresult of the object-oriented SVM is shown in (e) and the SVMMV is shown in (f)

3 Experimental Results and Analysis

To evaluate the performance of the proposed classificationapproach two subsets of high-spatial-resolution remote sens-ing imagesQuickBird and SPOT-5 satellite images were usedin a series of experiments and comparative analyses

To analyze the advantages of the proposed method forhigh-spatial-resolution remote sensing images traditionalpixel-based SVM object-oriented SVM and the method

proposed in [4] (SVM+MajorityVoting SVMMV)on fusionimages were used for comparison The panchromatic andmultispectral images were fused by the PANSHARP methodin PCI software Labeled watershed transformation wasapplied to themorphology gradient of panchromatic image toobtain segmentation regions The object-oriented SVM wasapplied on the panchromatic segmentation regions and theregion feature was computed from the fusion images TheSVMMV method presented a spectral-spatial classification


RoadWaterForest land Residential land

Agricultural landBare land Industrial land

Unclassified

(d) (e) (f)

(a) (b) (c)

Figure 5 SPOT-5 images and classification results (a) is the fused pseudocolor synthetic image (b) is the primitive mapped classificationimage (c) shows the final classification result obtained by the proposed method and (d) is result of the pixel-based SVM The classificationresult of the object-oriented SVM is shown in (e) and the SVMMV is shown in (f)

Table 1 Optimal parameters of all SVM classification experiments

Optimal parameters QuickBird dataset SPOT-5 dataset119862 120574 119862 120574

Pixel-based SVM 32768 8 2048 8Object-oriented SVM 32768 003125 2048 05SVMMV 32768 8 2048 8The proposed method 8192 2 32 8

scheme combining the pixel-based support vector machineclassification results and the watershed segmentation regionsthrough majority voting

The SVM classifier with Gaussian radial basis function(RBF) kernel was applied in all experiments The opti-mal parameters C (parameter that controls the amount ofpenalty during the SVM optimization) and 120574 (parameterthat describes the spread of the RBF kernel) were chosen byfivefold cross validation [11 19] Table 1 reports the optimalparameters of all SVM classification experiments

To assess classification accuracy a ldquoconfusion matrixrdquo isused Confusion matrices are obtained by selecting pointswith stratified random sampling and assessing the class ofeach point as calculated by each of the four methodsThe ref-erence classification images were generated through a precisemanual interpretation on fusion images The producer user

and overall classification accuracies are calculated from theldquoconfusion matrixrdquo

The first classification experiment was performed withthe QuickBird image including the panchromatic band witha resolution of 06m and four multispectral bands with aresolution of 24m The size of multispectral image is 256 times256 whereas that of the panchromatic image is 1024 times 1024The optimal parameters of SVM classifiers on QuickBirddataset are shown in Table 1 The classification results areshown in Figure 4 Table 2 shows the producer user andoverall classification accuracies for the QuickBird Imageclassification

A second classification experiment was performed withthe SPOT-5 satellite image consisting of a panchromatic bandwith a resolution of 25m and four multispectral bands witha resolution of 100m The size of the multispectral image is300 times 300 whereas it is 1200 times 1200 for the panchromaticimage The optimal parameters of SVM classifiers on SPOT-5 dataset are shown in Table 1 The results of classificationare shown in Figure 5 Table 3 shows the producer userand overall classification accuracies for the SPOT-5 imageclassification

By comparing the accuracies of the various classificationsthe proposedmethod based on a spatial mappingmechanismand a strategy of reclassification can be seen to obtain betterclassification results than pixel-based SVM object-orientedSVM and SVMMV Tables 2 and 3 show that the overall


Table 2 Producer and user classification accuracies for QuickBird image classification

ClassProposed method Pixel-based SVM Object-oriented SVM SVMMV

Producerrsquosaccuracy

Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy

Road 8314 8024 4972 7921 5539 7788 5059 7937Building 7493 7298 6411 4080 5774 5572 6596 4825Vegetation 8851 9007 7846 9371 8448 9229 8356 9256Water 7405 9661 6697 8315 7405 9333 7285 9114Bare land 5705 5571 6302 3563 6493 3082 6442 3603Shadow 6800 7575 6363 6766 6253 8385 7433 7344Artificial grass 9478 5000 9174 4271 9391 3484 9261 5285Plastic track 9100 6816 9450 7714 9550 7100 9650 8319Average accuracy 7893 7369 7152 6500 7357 6747 7510 6960Overall accuracy 8101 6901 7248 7304

Table 3 Producer and user classification accuracies for SPOT-5 image classification



Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy

Forest land 9300 8857 7700 8851 8400 8317 8300 9651Residential land 8375 8724 8214 7731 9282 8579 8764 8276Bare land 6495 6848 5052 6447 4433 8113 4639 6923Industrial land 8400 9333 8200 9147 8900 9175 8400 9545Water 9000 9474 8800 9167 9400 8704 8100 9000Agricultural land 8033 8083 8075 7414 8033 8100 8613 7661Road 6900 8672 5200 6933 5800 7160 5400 8302Average accuracy 8072 8570 7320 7956 7750 8307 7459 8480Overall accuracy 8132 7449 7767 7769

accuracies of the proposed method are higher than those ofthe pixel-based SVM object-oriented SVM and SVMMVmethods The accuracies of the pixel-based SVM resultswere lower than other three methods because the techniquesuffers from salt-and-pepper effects Some of the noise in thepixel-based classification result can be reduced through post-processing (eg majority filtering) but postprocessing canresult in the dislocation of class boundaries and influence theoutcome of subsequent applications The SVMMV methodwhich combined the pixel-based SVM classification resultsand the watershed segmentation regions through majorityvoting got higher classification accuracies than pixel-basedSVM classification The salt-and-pepper effects had beenreduced andmore homogeneous regionswere obtained in theSVMMV classificationmapsThe proposedmethod obtainedhigher accuracies than the SVMMVmethod because it madefull use of the spatial relationship between panchromaticand multispectral images and a strategy of reclassificationAlthough the object-oriented SVM pixel-based SVM andSVMMV classification results were obtained from a very-high-spatial-resolution fusion image the image was never-theless an estimation that could introduce spectral distortionand confusion

In order to make further comparative analysis of theclassification accuracy the area percents of each class forthe four methods on the QuickBird dataset were computed(Figure 6) Note that the class area percents of the proposedmethod are the closest to the real area percents

According to the experimental results the proposedclassificationmethod based on a spatial mappingmechanismand reclassification strategy can obtain higher accuracy thanthe pixel-based SVM object-oriented SVM and SVMMVclassification methods The proposed method can make fulluse of the information both in panchromatic and multispec-tral bands and integrate the pixel-based and object-basedclassification methods

In this paper only spectral features in the images wereapplied to the classification process Further work is requiredto integrate textural features

4 Conclusions

A new high-spatial-resolution remote sensing image clas-sification method based on a spatial mapping mechanismand reclassification strategy has been presented in this paper


05

101520253035404550

Road

Build

ing

Vege

tatio

n

Wat

er

Bare

land

Shad

ow

Art

ifici

algr

ass

Plas

tictr

ack

Are

a (

)

Real area ()Proposed method ()Pixel-based SVM ()

Object-oriented SVM ()SVMMV ()

Figure 6 Area statistics of QuickBird data classification results

in which a pixel-based classification method and an object-based segmentation and classification method were inte-grated by a spatial mapping mechanism and reclassificationstrategy Furthermore the proposed method was applied onraw high-spatial-resolution remote sensing data instead offusion images Experimental results have demonstrated thatthe proposed method can make full use of the informationin both panchromatic and multispectral bands integrate thepixel-based and object-based segmentation and classificationmethods and obtain higher final classification accuracy

Acknowledgments

This work was sponsored by the National Natural Sci-ence Foundation of China under the Grant no 60972142the National Key Technology Support Program of China(2012BAH27B05) and the National Ecological EnvironmentSpecial Project (STSN-10-03) The authors wish to thank theanonymous reviewers who provided constructive commentsthat improved the quality and clarity of the paper

References

[1] J Yuan and G He ldquoA new classification algorithm for highspatial resolution remote sensing datardquo in Proceedings of theInternational Conference on Earth Observation Data Processingand Analysis (ICEODPA rsquo08) vol 7285Wuhan China Decem-ber 2008

[2] Z Chen G Wang and J Liu ldquoA modified object-orientedclassification algorithm and its application in high-resolutionremote-sensing imageryrdquo International Journal of Remote Sens-ing vol 33 no 10 pp 3048ndash3062 2012

[3] T Novack T Esch H Kux and U Stilla ldquoMachine learningcomparison betweenWorldView-2 and QuickBird-2-simulatedimagery regarding object-based urban land cover classificationrdquoRemote Sensing vol 3 no 10 pp 2263ndash2282 2011

[4] Y Tarabalka J Chanussot J A Benediktsson J Angulo andM Fauvel ldquoSegmentation and classification of hyperspectraldata using watershedrdquo in Proceedings of the IEEE International

Geoscience and Remote Sensing Symposium pp III652ndashIII655Boston Mass USA July 2008

[5] M Dalla Mura J A Benediktsson B Waske and L Bruz-zone ldquoMorphological attribute profiles for the analysis of veryhigh resolution imagesrdquo IEEE Transactions on Geoscience andRemote Sensing vol 48 no 10 pp 3747ndash3762 2010

[6] L Bruzzone and L Carlin ldquoA multilevel context-based systemfor classification of very high spatial resolution imagesrdquo IEEETransactions on Geoscience and Remote Sensing vol 44 no 9pp 2587ndash2600 2006

[7] C Unsalan and K L Boyer ldquoClassifying land development inhigh-resolution panchromatic satellite images using straight-line statisticsrdquo IEEE Transactions on Geoscience and RemoteSensing vol 42 no 4 pp 907ndash919 2004

[8] B Salehi Y Zhang M Zhong and V Dey ldquoObject-basedclassification of urban areas using VHR imagery and heightpoints ancillary datardquo Remote Sensing vol 4 no 8 pp 2256ndash2276 2012

[9] M Pesaresi and J A Benediktsson ldquoA new approach forthe morphological segmentation of high-resolution satelliteimageryrdquo IEEE Transactions on Geoscience and Remote Sensingvol 39 no 2 pp 309ndash320 2001

[10] G Mountrakis J Im and C Ogole ldquoSupport vector machinesin remote sensing a reviewrdquo ISPRS Journal of Photogrammetryand Remote Sensing vol 66 no 3 pp 247ndash259 2011

[11] C-C Chang and C-J Lin ldquoLIBSVM a library for supportvector machinesrdquo ACM Transactions on Intelligent Systems andTechnology vol 2 no 3 article 27 2011

[12] G Fu H Zhao C Li and L Shi ldquoSegmentation for high-resolution optical remote sensing imagery using improvedquadtree and region adjacency graph techniquerdquo Remote Sens-ing vol 5 no 7 pp 3259ndash3279 2013

[13] C Zheng LWang R Chen andX Chen ldquoImage segmentationusing multiregion-resolution MRF modelrdquo IEEE Geoscienceand Remote Sensing Letters vol 10 no 4 pp 816ndash820 2013

[14] Y Tarabalka J Chanussot and J A Benediktsson ldquoSegmenta-tion and classification of hyperspectral images using watershedtransformationrdquo Pattern Recognition vol 43 pp 2367ndash23792010

[15] G Wang and G He ldquoHuman visual system based processingfor high resolution remote sensing image segmentationrdquo in Pro-ceedings of the 2nd International Conference on Signal ProcessingSystems (ICSPS rsquo10) pp V1474ndashV1478 Dalian China July 2010

[16] G Wang G He and J Liu ldquoA new classification method forhigh spatial resolution remote sensing image based onmappingmechanismrdquo in Proceedings of the International Conference onGeographic Object-Based Image Analysis (GEOBIA rsquo12) pp 186ndash190 Rio de Janeiro Brazil May 2012

[17] A G Wacker and D A Landgrebe ldquoMinimum distanceclassification in remote sensingrdquo Tech Rep 25 LARS 1972

[18] A K Shackelford and C H Davis ldquoA hierarchical fuzzyclassification approach for high-resolution multispectral dataover urban areasrdquo IEEE Transactions on Geoscience and RemoteSensing vol 41 no 9 pp 1920ndash1932 2003

[19] Y Tarabalka J Chanussot and J A Benediktsson ldquoSegmenta-tion and classification of hyperspectral images using minimumspanning forest grown from automatically selected markersrdquoIEEE Transactions on Systems Man and Cybernetics B vol 40no 5 pp 1267ndash1279 2010

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for hyperspectral images combining the pixel-based sup-port vector machine classification results and the water-shed segmentation regions together Bruzzone and Carlin[6] proposed a novel pixel-based system for the classifica-tion of high-spatial-resolution images The spatial contextinformation of each pixel extracted from multilevel seg-mentation images was used to obtain more accurate andreliable classification result Chen et al [2] introduced amodified object-oriented classification algorithm integrat-ing multicharacteristics of high-spatial-resolution remotesensing image Unsalan and Boyer [7] put forward a newclassification method of land development in high resolutionpanchromatic satellite images using straight line statisticsSalehi et al [8] developed a hierarchical rule-based object-based classification framework coupledwith height points forcomplex urban environment classification The rule set wasextracted from a training set of QuickBird image coupledwith a layer of height points In addition the morphologicalbased segmentation and classification was investigated in thework of Dalla Mura et al [5] and Pesaresi and Benediktsson[9] Although some improved algorithms can increase theclassification accuracy by making use of spectral and texturalinformation the classification results still cannot satisfy allactual needs Most of these methods were developed onfusion images or multispectral images The spatial relation-ship of high-spatial-resolution remote sensing image betweenpanchromatic and multispectral bands has not been fullyconsidered

This paper proposes a newhigh-spatial-resolution remotesensing image classification method based on a mechanismof spatial mapping and a strategy of reclassification Theanalysis makes use of both spectral and spatial informationfrom high-spatial-resolution remote sensing data The clas-sification framework uses a spatial mapping mechanism tofit the special data format and the content of high-spatial-resolution remote sensing imagesThis algorithm is not basedon a fusion image but rather on raw high-spatial-resolutionremote sensing data which makes full use of the spatialresolution relationship of panchromatic and multispectralimages

QuickBird and SPOT-5 satellite data were employed in aseries of experiments and comparative analyses Traditionalpixel-based SVM object-oriented SVM and the methodproposed in [4] (SVM + Majority Voting SVMMV) wereused for comparison to analyze the advantage of the pro-posed method Experimental results show that a classifica-tion method based on a spatial mapping mechanism andreclassification strategy for high-spatial-resolution remotesensing data can make full use of the information in bothpanchromatic and multispectral bands integrate the pixel-and object-based classification methods and improve theclassification accuracy

2 Methodology

This section details the support vector machine classificationmethod andwatershed image segmentation and outlines thespatial mapping mechanism and reclassification strategy A


Pixel-based classification Image segmentation

Mapping classification result from multispectral to panchromatic

Reclassify unclassified regions

Final panchromatic classification result

Figure 1 The flow chart of the proposed classification method

flow chart of the proposed classification method is shown inFigure 1

21 Support Vector Machine Classification The first stepin the proposed method is the pixel-based classificationof a multispectral image There are many possible imageclassification algorithms for remote sensing images Eachalgorithm has its unique advantages and weaknesses In thispaper we focus on the application of the spatial mappingmechanism and reclassification strategy for high-spatial-resolution remote sensing images In order to improve theoverall efficiency of approach and to simplify it a supportvector machine classifier is chosen Theoretically any pixel-based classification method can be applied in the proposedmethod

Support vector machine (SVM) is a supervised non-parametric statistical learning technique and widely usedin classification of remote sensing images [10] SVM iswell at solving nonlinear high dimensional and limitedtraining samples [8] In this paper LIBSVM library is usedto implement the SVM classification algorithm [11]

22 Watershed Image Segmentation Image segmentationis an important part of image interpretation especiallyfor high-spatial-resolution remote sensing images [12 13]High-spatial-resolution remote sensing images contain moreinformation of ground objects and show great diversity ofthem The purpose of segmentation is to divide an imageinto homogeneous regions Watershed transformation is apowerful mathematical morphology technique for imagesegmentation [4 14] A watershed algorithm is a good choicefor high-spatial-resolution remote sensing images because ofits fast segmentation speed [15]

In this paper a labeledwatershed segmentation algorithmwas used to segment the panchromatic imageMorphologicaloperators were used to characterize the gradient of the imagewhich enabled the watershed segmentation algorithm to labelareas within the image







119894 1199101015840119895)

where 1199091015840119894= Int(119909

1198944 + 04) and 1199101015840

119895= Int(119910

1198954 + 04) The










119863ED = (119899

sum119894=1

(119909119894minus 119862119894)2)

12

(1)





a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

a

a

a

a

a

a

a

b

b

b

b

b

b

b


(a)

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

2 2 2 2

1 1 1 1 2 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2


(b)


(a) (b) (c)

(d) (e) (f)












Unclassified

(d) (e) (f)

(a) (b) (c)
















Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy





Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy






4 Conclusions



05

101520253035404550

Road

Build

ing

Vege

tatio

n

Wat

er

Bare

land

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Art

ifici

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Are

a (

)





Acknowledgments


References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















119894 1199101015840119895)

where 1199091015840119894= Int(119909

1198944 + 04) and 1199101015840

119895= Int(119910

1198954 + 04) The










119863ED = (119899

sum119894=1

(119909119894minus 119862119894)2)

12

(1)





a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

a

a

a

a

a

a

a

b

b

b

b

b

b

b


(a)

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

2 2 2 2

1 1 1 1 2 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2


(b)


(a) (b) (c)

(d) (e) (f)












Unclassified

(d) (e) (f)

(a) (b) (c)
















Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy





Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy






4 Conclusions



05

101520253035404550

Road

Build

ing

Vege

tatio

n

Wat

er

Bare

land

Shad

ow

Art

ifici

algr

ass

Plas

tictr

ack

Are

a (

)





Acknowledgments


References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a

a a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

b

b

b

b

b

b

b

a

a

a

a

a

a

a

a

b

b

b

b

b

b

b


(a)

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

2 2 2 2

1 1 1 1 2 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2

1 1 1 22 2 2 2


(b)


(a) (b) (c)

(d) (e) (f)












Unclassified

(d) (e) (f)

(a) (b) (c)
















Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy





Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy






4 Conclusions



05

101520253035404550

Road

Build

ing

Vege

tatio

n

Wat

er

Bare

land

Shad

ow

Art

ifici

algr

ass

Plas

tictr

ack

Are

a (

)





Acknowledgments


References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












Unclassified

(d) (e) (f)

(a) (b) (c)
















Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy





Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy






4 Conclusions



05

101520253035404550

Road

Build

ing

Vege

tatio

n

Wat

er

Bare

land

Shad

ow

Art

ifici

algr

ass

Plas

tictr

ack

Are

a (

)





Acknowledgments


References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy





Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy


Userrsquosaccuracy






4 Conclusions



05

101520253035404550

Road

Build

ing

Vege

tatio

n

Wat

er

Bare

land

Shad

ow

Art

ifici

algr

ass

Plas

tictr

ack

Are

a (

)





Acknowledgments


References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










05

101520253035404550

Road

Build

ing

Vege

tatio

n

Wat

er

Bare

land

Shad

ow

Art

ifici

algr

ass

Plas

tictr

ack

Are

a (

)





Acknowledgments


References























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









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