Contextual Classification of Hyperspectral Remote Sensing Images Using SVM-PLR

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1 Australan Journal of Basc and Appled Scences, 5(8): , 2011 ISSN Contextual Classfcaton of Hyperspectral Remote Sensng Images Usng SVM-PLR 1 Mahd hodadadzadeh, 2 Hassan Ghasseman 1,2 Faculty of Electrcal and Computer Engneerng, Tarbat Modares Unversty, Tehran, Iran. Abstract: In ths paper, we propose a novel contextual classfcaton of hyperspectral data. We use probablstc label relaxaton (PLR) process to ncorporate context nformaton nto the spectral pxelwse classfcaton procedure. In conventonal PLR procedure, frst a maxmum lelhood classfcaton s performed and class probabltes are computed by usng multvarate normal models. However ths method s not effcent for hyperspectral data wth lmted tranng samples. In ths paper we suggest to use support vector machne (SVM) n order to ntal classfcaton and also use class probablty estmates whch are obtaned from SVM classfcaton for PLR postprocess. We call ths proposed method as SVM-PLR. Expermental results are presented for two types of hyperspectral mages, agrcultural and urban data. The proposed method mproves dramatcally classfcaton accuraces, when compare to spectral pxelwse classfcaton. Moreover our proposed method can mprove performance of conventonal PLR postprocess for hyperspectral data. ey words: Hyperspectral mages, contextual classfcaton, probablstc label relaxaton (PLR), support vector machne (SVM). INTRODUCTION Hyperspectral magery records a detaled spectrum of lght arrvng at each pxel (C.-I. Chang, 2007). The rch spectral nformaton of hyperspectral data has made t possble to dstngush materals and land cover classes present on the Earth s surfaces. One of the most mportant tass for hyperspectral mages s classfcaton whch s a method by whch labels may be attached to pxels n vew of ther spectral character (J. A. Rchards and X. Ja, 2006). More than 100 spectral channels n hyperspectral mages mprove overall mage classfcaton accuracy; however ths number of spectral channels presents some constrants. One of the most mportant constrants s lmted tranng samples of hyperspectral mages. Whle t s often stated that the number of tranng samples for each class should comprse at least tmes the number of wavebands, ths quantty s precous and unavalable for hyperspectral data (Foody and Mathur, 2006). Varous classfcaton technque have been proposed that label a pxel on the bass of ts spectral propertes alone, wth no attenton to labels whch are assgned to neghborng pxels. However, n remote sensng mages the pxels n the neghborhood are lely to assgn the same labels, because magng sensors acqure sgnfcant portons of energy from adjacent pxels and because ground cover types generally occur over a regon that s large compared wth the sze of a pxel (J. A. Rchards and X. Ja, 2006). Therefore ntegraton of contextual and spectral nformaton can mtgate the problem of nsuffcent number of tranng samples. Landgrebe and hs research group were poneers n usng spatal nformaton for mprovng remote sensng mage classfcaton (R. ettg and D. Landgrebe, 1976). Durng recent years, varous approaches have been dscussed to ncorporate contextual nformaton (Q. Jacson and D. Landgrebe, 2002; P. Zhong and R. Wang, 2007; A. Plaza, J. A. Benedtsson, 2009; Y. Tarabala and J. A. Benedtsson, 2009; M. hodadadzadeh and H. Ghasseman, 2010). One category of these approaches s to perform postprocess after the mage has been classfed by a pxelwse classfer. The postprocessng flters and post-classfcaton approaches le Probablstc Label Relaxaton (PLR) whch use fxed-wndow for ncorporatng contextual nformaton are n ths category. In conventonal PLR procedure, frst a maxmum lelhood classfcaton s performed and class probabltes are computed by usng multvarate normal models. However, the effectveness of maxmum lelhood classfcaton depends upon reasonably accurate estmaton of the mean vector and the covarance matrx for each spectral class. Consstent estmaton s dependent upon havng a suffcent number of tranng samples that s not avalable for hyperspectral mages. It s also sgnfcant to note that classfcaton tme, ncreases quadratcally wth number of spectral components for the maxmum lelhood classfer (J. A. Correspondng Author: Hassan Ghasseman, Faculty of Electrcal and Computer Engneerng, Tarbat Modares Unversty, Tehran, Iran E-mal: ghassem@modares.ac.r. 374

2 Aust. J. Basc & Appl. Sc., 5(8): , 2011 Rchards and X. Ja, 2006). SVMs have shown good performance n hyperspectral data classfcaton partcularly n small tranng samples sze. SVM s orgnally a bnary classfer whle n remote sensng applcaton we face to several classes of nterest. Moreover SVMs produce a value that s not a probablty. Varous approaches have been proposed to allevate these problems (T.-F. Wu, C.-J. Ln and R. C. Weng, 2004; M. Gonen and A. G. Tanugur, 2008; A. Mathur and G. M. Foody, 2008). In ths paper we propose to use a precous technque that provde mult-class probablty estmates for SVM classfer and then by usng these class probablty estmates, perform PLR after SVM classfcaton to ncorporate context. Usng PLR postprocessng method based on SVM classfer (SVM-PLR), has shown better performance n comparson wth tradtonal ML-PLR classfcaton scheme. The bloc dagram of the proposed method s shown n Fg. 1. Fg. 1: Bloc dagram of the proposed approach Two hyperspectral arborne mages were used to demonstrate expermental results: a 103-band Reflectve Optcs System Imagng Spectrometer (ROSIS) mage of the Unversty of Pava, Italy, and a 220-band AVIRIS mage taen over the Northwestern Indana s Indan Pne ste. Although the proposed method has been tested on these hyperspectral mages, the proposed method s general and can be appled for other types of data as well. Probablstc Pxelwse Classfcaton: The frst step s to perform a probablstc pxelwse classfcaton of the hyperspectral mage. We propose to use a SVM classfer and then compute probablty estmates for multclass classfcaton. SVMs search for optmal separatng hyperplane wth maxmal dstance between the hyperplane and the nearest samples (support vectors) from each of the two classes. We brefly descrbe SVM algorthm for a supervsed bnary classfcaton problem as followng. d Let us assume that we have N tranng vectors from d dmensonal feature space x ( 1,2,, N) whch labeled wth y 1, 1 Functon yˆ sgn[ f( x)] s used for mang decson on nput feature vector x, where f(x) s dscrmnant functon of SVM whch s gven as f ( x) wx. b, (1) where w s weght vector perpendcular to separatng hyperplane and b s a bas. These values can specfy a hyperplane and can be calculated by followng optmzaton problem 1 2 N mnmze: 2 w C 1 subjected to: y( wx. b) 1 0, 1, 2,, N, (2) hear, C s the penalty parameter whch control the trade-off between tranng accuracy and generalzaton and s are slac varables that ntroduced n order to deal wth msclassfed vectors. The optmzaton problem (2) can be solved usng Lagrange multplers and then becomes 375

3 Aust. J. Basc & Appl. Sc., 5(8): , 2011 N N N 1 mnmze: 1 2 (. ) 1 1 jyy j xx j j N subjected to: 0 and 0, j 1 y C 1, 2,, N. By replacng nner products ( xx. ) j wth a ernel functon that fulfll Mercer s condton le Gaussan radal bass functon (4), the fnal dscrmnant functon f(x) s defned as (5), whch s belongng to the category of nonlnear dscrmnant functon. 2 j j ( x, x ) exp( x x ) f ( x) y( x, x) b. In (J. Platt, 1999) two precous technques are proposed for computng mult-class probablty estmates based on combnng all parwse comparsons. We use one whch s descrbed as followng. In ths technque we want to estmate, for each pxel x, the probabltes of belongng to each class as p p p( x), 1,,. The probabltes are computed by solvng the followng optmzaton problem that has a unque soluton and can be solved by a lnear system p mn r p r p p 2 j j j 1 j: j subjected to p 1, p 0,, 1 where p s the set of probablty estmates and r are parwse class probabltes that r r 1, j. j It s clear that for approxmatng parwse class probabltes, subset of tranng samples of total that d belongs to and j classes, ( 1,2,, L) s consdered, whch labeled by y 1, 1. Platt s x algorthm s used to solve ths two class problem whch s proposed to approxmate a posteror class probablty by a sgmod functon 1 rj Pr( y 1 x), (8) Af B 1 e where f = f(x) s a decson value that produced by SVM classfer (5). Let for each tranng sample j j (3) (4) (5) (6) (7) p Pr( y 1 x ) then, by mnmzng the followng negatve log-lelhood functon the best parameters ( A, B ) can be determned as L ( A, B ) mn t log( p) (1 t)log(1 p), ( AB, ) 1 (9) where t s the MAP estmate for target probablty that can computed as N 1 f y 1 N 2 t, for 1,2,, L, 1 f y 1 N 2 (10) 376

4 Aust. J. Basc & Appl. Sc., 5(8): , 2011 where N + and N - are number of y s postve and negatve respectvely. An mproved mplementaton of Platt s algorthm s presented n (H. Ln and C. Ln, 2003) whch s theoretcally converges and avods numercal dffcultes. Probablstc Label Relaxaton: Probablstc Label Relaxaton (PLR) s a postprocess procedure whch s used fxed-wndow to ncorporate contextual nformaton and assgn the most approprate class for each pxel. We descrbe PLR algorthm as followng. Let the set of probabltes for a pxel (m) represented by p ( ) 1,2,,, where p ( ) 1. m m 1 Suppose that a neghborhood Nm s defned surroundng pxel m as shown n Fg. 2. At th teraton, the correct set of label probabltes for the pxel m that havng taen account both of spectral and contextual nformaton s defned as 1 pm( ) Qm( ) pm ( ), p ( ) ( ) 1 m Qm here Qm( ) s neghborhood functon at th teraton that can be defned as Qm( ) dn pmn( j) pn( j), nnm j1 where d n s are neghbor weghts that recognze that some neghbors may be more nfluental than others and pmn ( j ) s the capablty measure that can be estmated drectly from ntally classfcaton map. Unless there s good reason to do otherwse the neghbor weghts are generally chosen all to be the same (J. A. Rchards and X. Ja, 2006). Equaton (12) s appled as many tmes as necessary to ensure that p ( ) m have stablzed. Fg. 2: Defnton of neghborhood wth assgned neghbor weghts. (a) four neghborhood system, (b) eght neghborhood system Expermental Results: We use two types of hyperspectral arborne mages to demonstrate expermental results, agrcultural and urban, whch are Indana mage and Unversty of Pava mage respectvely. Indana Image: The hyperspectral data used n the frst experment was recorded by AVIRIS sensor over the Indana Pnes test ste n Northwestern Indna. The mage has spatal dmenson of pxels, and the spatal resoluton s 20 m per pxel. Ths data has orgnally 220 spectral bands, however due to the effect of atmospherc phenomena, 20 spectral bands ( , , and 220) were dscarded. A three-band false color mage and the avalable groundtruth map data are shown n Fg. 3(a) and Fg. 3(b) respectvely. 377

5 Aust. J. Basc & Appl. Sc., 5(8): , 2011 Fg. 3: Indana mage. (a) Three-band color composte (b) Reference Map The avalable orgnal groundtruth map data contan sxteen classes of dfferent types of crops, however sx classes (alfalfa, corn, grass/pasture-mowed, oats, bldg-grass-tree-drves, stone-steel towers) that contan a small number of samples (less than 380 samples) n the groundtruth map are dscarded. The remanng classes wth the numbers of test and tranng samples for each class are detaled n Table. I. The tranng set s chosen randomly from groundtruth mage and the remanng samples are consdered as the test set. Frst as shown n Fg. 4 ntal SVM classfcaton was performed usng the multclass parwse (one versus one) SVM classfer, wth the Gaussan RBF ernel. The parameters C and γ were determned by fvefold cross valdaton, whch gave C = 1024 and 2 7. Then the probabltes estmates were obtaned usng equaton (7) and based on them, the fnal classfcaton map (see Fg. 4(d)) was obtaned after postprocessng usng proposed PLR method. Table. I show the class-specfc accuraces (that s the percentage of correctly classfed pxels for each class) for the ten dfferent land-cover classes as well as Overall accuracy (OA), average accuracy (AA) and appa coeffcent (). We acheve 89.34% OA, 91.32% AA, and 87.69% value n ths data; where these values are 77.32%, 82.07%, and 74.09% for SVM classfcaton, respectvely. The classcal PLR algorthm usng the multvarate normal models to compute class probabltes, n order to ncorporate spatal context nto a preexstng ML classfcaton. Therefore a feature reducton algorthm s appled to reduce the spectral dmenson of pxel vectors n ths data. Because large dmensonalty of feature vectors n hyperspectral data may cause the problem of the covarance matrx sngularty or naccurate parameter estmaton results n ML classfcaton. We proposed to use PCFA (A. Jensen and A. Solberg, 2007) method to reduce the 200 spectral bands of ths data to ten. In experments ths method showed better performance than the other common methods le PCA and ICA for feature reducton n ths data. As detaled n Table. I, ML classfcaton shows low classfcaton accuraces, for example Soybeans-mn tll class whch descrbes mostly large regons n the mage, shows classfcaton accuracy whch s not acceptable. After PLR process on ths ML classfcaton map, the classfcaton accuraces are mproved n a range of 0.2% 25.57%. Although ntegraton of spatal and spectral nformaton ncreases classfcaton accuraces n the most of the classes, these values are stll not proper for classes le Corn-mn tll, Soybeans-mn tll and Woods. By comparng classfcaton accuraces resulted from our proposed PLR method to tradtonal PLR method whch s based on ML classfcaton, we consdered that for those mentoned classes wth low classfcaton accuraces, these values were ncreased dramatcally when the class probabltes obtaned from SVM classfcaton are used n the PLR procedure. In partcular, the class Soybeans-mn tll s much more accurately classfed when the proposed method s used (mprovement of the classfcaton accuracy by 27.96%). The fnal classfcaton maps are shown n Fg. 5. The use of contextual nformaton by performng PLR process sgnfcantly reduces nose n the classfcaton map whch leads to mproved classfcaton accuraces. Good classfcaton results n ths agrcultural hyperspectral data shows that, the proposed approach s partcularly sutable for classfcaton of mages contanng large spatal structures. 378

6 Aust. J. Basc & Appl. Sc., 5(8): , 2011 Fg. 4: Indana mage, the result of (a) ML, (b) ML-PLR, (c) SVM and (d) SVM-PLR classfcaton Table 1: Classes, number of samples, and class specfc accuraces n percentage for the Indana mage Class No. of Samples 10 spectral bands 200 spectral bands No Name Test Tran ML ML+PLR SVM SVM+PLR 1 Corn-no tll Corn-mn tll Grass/pasture Corn Hay-wndrowed Soybeans-no tll Soybeans-mn tll Soybeans-clean tll Wheat Woods Overall Accuracy (OA) Average Accuracy (AA) appa () Unversty of Pava Image: The Unversty of Pava mage was recorded by the ROSIS optcal sensor over the urban area of the Unversty of Pava. The orgnal spatal sze of mage s pxels wth a spatal resoluton of 1.3 m/pxel Here we are nterested only n a part of ths mage ( ) whch contans all nne classes of nterest (Asphalt, Meadows, Gravel, Trees, Meta sheets, Bare sol, Btumen, Brcs, Shadows). A three-band false color mage and the groundtruth map data are shown n Fg. 5(a) and Fg. 5(b) respectvely. The tranng set s chosen randomly from groundtruth mage and the remanng samples are consdered as the test set. The orgnal mage has 115 spectral bands however 12 most nosy bands were dscarded and as Indana mage, for comparng our proposed PLR method to tradtonal PLR, we reduce the remanng spectral bands to ten by usng the method of PCFA (A. Jensen and A. Solberg, 2007). Multclass, one versus one, SVM classfcaton was performed on the orgnal mage usng the Gaussan RBF ernel. The parameters C and γ were determned by fvefold cross valdaton, whch gave C = 128 and γ = for ths data. After the conventonal SVM pxelwse classfcaton, the probabltes estmates were obtaned usng equaton (7). Then the fnal classfcaton map was obtaned after postprocessng usng proposed PLR method. Table. II gve the class-specfc and the global classfcaton accuraces, respectvely, for the ML 379

7 Aust. J. Basc & Appl. Sc., 5(8): , 2011 and SVM classfcaton, wthout and wth the PLR step. The classfcaton maps for the pxel wse and the contextual classfcaton after the PLR are shown n Fg. 6. Fg. 5: Part of the unversty of pava mage. (a) Three-band color composte (b) Reference Map The SVM classfer correctly classfed 93.91% of pxels from the test set whle ths value s 82.28% for ML classfcaton. The best classfcaton accuraces are acheved for the classes Trees, Meta sheets and Shadows. About classes Meadows and Bare sol, mostly large regons n the mage, the accuraces are mproved by 13.32% and 25.99% respectvely, compared to the pxelwse ML classfcaton. The accuraces were consderably mproved after PLR postprocess, wth the mprovement beng more sgnfcant when ths step s performed after a pxel wse SVM classfcaton. As detaled n Table. II the proposed contextual classfcaton mproves the classfcaton accuraces for almost all the classes, except for the class Trees. For ths class, the dfference s not sgnfcant and the accuracy s hgh enough. For the other classes, classfcaton accuraces are mproved n a range of 0.96% 21.68%. The best global accuraces are obtaned when performng our proposed PLR procedure. We acheve 98.58% OA, 98.14% AA, and 98.28% value n ths data; Where these values are 93.91%, 94.06%, and 92.65% for SVM classfcaton, respectvely. As shown n Fg. 6, the use of contextual nformaton by performng PLR process sgnfcantly reduces nose n the classfcaton map. Specally n the case of ths data (that s a urban hyperspectral mage comprsed of complex boundares), these sgnfcant classfcaton results, show that, the proposed approach s also sutable for classfcaton of mages contanng small spatal structures. Fg. 6: Unversty of Pava mage, the result of (a) ML, (b) ML-PLR, (c) SVM and (d) SVM-PLR classfcaton 380

8 Aust. J. Basc & Appl. Sc., 5(8): , 2011 Table 2: Classes, number of samples, and class specfc accuraces n percentage for the unversty of pava mage Class No. of Samples 10 spectral bands 200 spectral bands No Name Test Tran ML ML+PLR SVM SVM+PLR 1 Asphalt Meadows Gravel Trees Meta sheets Bare sol Btumen Brcs Shadows Overall Accuracy (OA) Average Accuracy (AA) appa () Concluson: In ths paper, we propose a novel contextual classfcaton of hyperspectral data. We use probablstc label relaxaton (PLR) process to ncorporate context nformaton nto the spectral pxelwse classfcaton procedure. In conventonal PLR procedure, frst a maxmum lelhood classfcaton s performed and class probabltes are computed by usng multvarate normal models. However ths method s not effcent for hyperspectral data wth lmted tranng samples. In ths paper we suggest to use support vector machne (SVM) n order to ntal classfcaton and also use class probablty estmates whch are obtaned from SVM classfcaton for PLR postprocess. Expermental results are presented for two types of hyperspectral mages, agrcultural mage (that s an mage ncludng large spatal structures comprsed of flat areas) and urban data (that s a mage contanng small spatal structures comprsed of complex boundares). The proposed method mproves dramatcally classfcaton accuraces n both mages and provdes classfcaton maps wth more homogeneous regons, when compare to spectral pxelwse classfcaton. Moreover our proposed method can mprove performance of conventonal PLR postprocess for hyperspectral data. ACNOWLEDGEMENTS The authors would le to than Prof. Benedtsson of the Unversty of Iceland, Iceland and Prof. Gamba of the Unversty of Pava, Italy, for provdng the Pava data set. Ths wor was supported n part by the Iran Research Insttute for ICT-ITRC under contract No. 500/ REFERENCES Chang, C.-I., Hyperspectral Data Explotaton: Theory and Applcatons, Wley-Interscence. Foody, G.M. and A. Mathur, The use of small tranng sets contanng mxed pxels for accurate hard mage classfcaton: tranng on mxed spectral responses for classfcaton by a SVM, Remote Sens. Envron., 103(7): Ghasseman, H. and D. Landgrebe, Object-orented feature extracton method for mage data compacton, IEEE Control System Magazne, 8(3): Gonen, M., A.G. Tanugur and E. Alpaydm, Multclass posteror probablty support vector machnes, IEEE Trans. Neural Networs, 19(1): Jacson, Q. and D. Landgrebe, Adaptve bayesan contextual classfcaton based on marov random felds, IEEE Trans. Geosc. Remote Sens., 40(11): Jensen, A. and A. Solberg, Fast hyperspectral feature reducton usng pecewse constant functon approxmatons, IEEE Geoscence and Remote Sensng Letters, 4(4): ettg, R. and D. Landgrebe, Classfcaton of multspectral mage data by extracton and classfcaton of homogenous objects, IEEE Trans. Geosc. Remote Sens., 14: hodadadzadeh, M., R. Rajab and H. Ghasseman, Combnaton of regon-based and pxel-based hyperspectral mage classfcaton usng eroson technque and MRF model, In Proc. 18th Iranan Conf. on Electrcal Engneerng, pp , Isfahan, Iran. Ln, H., C. Ln and R.C. Weng, A note on plats probablstc outputs for support vector machnes, Dept. Comput. Sc., Nat. Tawan Unv., Tape, Tawan. 381

9 Aust. J. Basc & Appl. Sc., 5(8): , 2011 Mathur, A. and G.M. Foody, Multclass and bnary SVM classfcaton: Implcatons for tranng and classfcaton users, IEEE Geoscence and Remote Sensng Letters, 5(5): Platt, J., Probablstc outputs for support vector machnes and comparson to regularzed lelhood methods, n Advances n Large Margn Classfers, MA: MIT Press, Plaza, A., J.A. Benedtsson, J. Boardman, J. Brazle, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, J. A. Gualter, M. Marconcn, J.C. Tlton and G. Trann, 2009, Recent advances n technques for hyperspectral mage processng, Remote Sens. Envron., 113: Rchards, J.A. and X. Ja, Remote Sensng Dgtal Image Analyss, Sprnger-Verlag Berln Hedelberg. Tarabala, Y. and J. A. Benedtsson, Spectral-spatal classfcaton of hyperspectral magery based on parttonal clusterng technques, IEEE Trans. Geosc. Remote Sens., 47(8): Wu, T.-F., C.-J. Ln and R.C. Weng, Probablty estmates for mult-class classfcaton by parwse couplng, J. Mach. Learn. Res., 5: Zhong P. and R. Wang, A multple condtonal random felds ensemble model for urban area detecton n remote sensng optcal mages, IEEE Trans. Geosc. Remote Sens., 45(12):