Support Vector Machine for Remote Sensing image classification

Transcription

1 Support Vector Machne for Remote Sensng mage classfcaton Hela Elmanna #*, Mohamed Ans Loghmar #, Mohamed Saber Naceur #3 # Laboratore de Teledetecton et Systeme d nformatons a Reference spatale, Unversty of Tuns El Manar * Ecole Supereure de Communcaton de Tuns, Tunsa hela.elmanna@gmal.com MohamedAns.loghmar@s.rnu.tn 3 Saber.Naceur@nsat.rnu.tn Abstract The presented work deals wth remote sensng data classfcaton. The major goal s to provde the land characterzaton for multspectral mage observatons. Channel mages contan data acqured from dfferent wavelength wthn the frequency spectrum. Due to the multple radance reflecton, the land characterzaton n the observaton space became complex and neffcent. The goal of ths work s to perform a feature space for observatons. Then a statcally learnng classfer usng the Support Vector Machne s developed for a relable land characterzaton. eywords Fuson, Segmentaton, Classfcaton, Support Vector Machne, Feature extracton I. INTRODUCTION Remote sensng processng methods have been motvated by the growng number of channels and the spatal resoluton enhancement. Varous processng schemes and applcaton felds are based on mage algorthm and recognton methods. The presented work ams to land segmentaton and classfcaton for multspectral mage. We am to develop a fuson data scheme then a classfcaton tool based on learnng machnes. Frst we wll deduce a feature space from dfferent regon descrptors. Ths step s based on a fuson scheme from dfferent mage channel and from dfferent descrptors. Then regons wll be classfed nto land types. Classfcaton n a feature space gves better accuracy and avods learnng dvergence problems. II. MULTISPECTRAL DATA Multspectral mage are the collected radance n dfferent channel range from vsble to the near nfrared n the electromagnetc spectrum. Obtaned band mages reflect the land cover spectral response n dfferent wavelength. The band number reaches sx for some multspectral satellte. A huge number of narrowest bands characterze the hyperspectral satellte. Collected energy by sensors s n fact the result of many reflectons and accurate noses due to the atmosphere and the heterogeneous composton of the land. Many proposed model based on physcal assumptons ams to analyse the remote sensng scene. The fnal goal s mprovng classfcaton accuracy. Consderng the set of nstantaneous observatons from SPOT4 satellte denoted X. X(t)=[X (t),x (t),x 3 (t),x 4 (t)] () The band mages are correlated as presented n Table. Therefore, workng n a feature space s most effcent and relable. In fact, radance dstorton by atmosphere and the pxel heterogeneous composton produce much confuson and affect the classfcaton results. Consderng the presented scene n Fg. located n north Tunsa. The scene sze s 3000x3000 and the spatal resoluton s 0x0 m. The land cover s heterogeneous. Man classes are urban areas, agrcultural parcel, lakes, wetlands and mountans. Classfyng the land cover n a feature space needs to fnd the sutable descrptors combnaton that descrbes the presented classes. Many works uses the wavelet transform and combne two or more types of descrptors. The next part presents the feature space concept and descrpton. TABLE. BANDS CORRELATION Band Band Band 3 Band 4 Band Band Band Band

2 fuson wll be performed by regon. Segmented regon wll be classfed n the upper stage basng on the feature fuson. The last fuson level concerns the decson fuson by a knd of vote or correlated decson functon from each elementary decson. Fg.. Composte mage. Fg.. Fuson Levels. III. REGION DESCRIPTORS AND DATA FUSION SCHEMES When the observaton data are the result of many dstortons and non lnear mxture, t s sutable to fnd another space of presentaton. The obtaned space s based on a set of feature descrptors and provdes better presentaton for the land cover. Fuson system s also relable n case of correlated data and redundancy n the observaton space. Many data descrptors have been developed for data recognton, detecton and estmaton. To fnd whether the descrptors are suffcent to descrbe data stlls the man problem. Exstent works uses experments and comparsons. Combnng dfferent descrptors gves also a relable data representaton wthn the feature space. Although the feature space have greater dmenson, the classfcaton thematc became more effcent and relable. Exstent descrptors are pxel orented and regon orented. Regon descrptors descrbe the regon shape or the regon content. Wavelets have been wdely used for recognton and detecton applcatons. It conssts on transformng orgnal data to many frequences and scales []. Each transform gves a new data presentaton and therefore new data characterzaton. Other descrptors are based on Fourer transform, mage moments or gradent orentated hstogram. For regon content descrptors, texture characterzaton has proved to be effcent when the mage classes have a unform and repettve appearance. Wavelet and specally Gabor transform are the prncple tools for texture classfcaton. Gabor fltered mages gve a space-scale analyss for the textured mage []. For multband mages, the way that we manage the feature extractor process produces dfferent fuson scheme. Whether we extract feature before or after data fuson and whether we take a decson n front-end or n the back-end of data process produces many fuson levels. Manly fuson levels are summarzed n Fg. 3 [3]. Fuson can concerns only basc data whch are observatons n our case. Therefore, segmentaton and other processng method deal wth a combnaton of data. Ths method s generally pxel based [4]. For regon based algorthm, the segmentaton and feature extracton s performed for each data source. Then, IV. CLASSIFICATION METHOD BASED ON SUPPORT VECTOR MACHINE The Support Vector Machne (SVM) classfcaton process s based on fndng an optmal separaton hyperplan performng the mnmum dstance. The basc case s a bnary classfcaton n class + or -. Let consderate the learnng data base contanng k coupes {c, v} where c s the class label c {-,+} and v the feature vector. The optmal hyperplan s defned by a subset of feature vectors from the learnng database named Support vectors denoted V. The classfcaton problem s equvalent to a quadratc optmzaton wth constrants [5]. Fg. 4 shows the basc lnear separablty between two classes by hyperplan. The optmzaton problem s parameterzed by a penalty parameter C that descrbes the separaton complexty and the classfcaton error. The optmzaton problem under constrants s expressed by Eq. : ω ( α ) = where c α = 0 ( v, v ) α α j cc jφ j α, j = = () = and 0 α C When the separablty s nonlnear as presented n Fg. 5, a nonlnear transform from the feature space to a new space wth greater dmenson allows a lnear separablty. There s no need to fnd the transform functon, only a kernel functon s needed. The kernel choce s determnatve for the separablty and depends on the classfcaton applcaton. The optmal soluton defnes the support vectors V. The decson functon s the sgn of gven n Eq. 3. = V α c ( v, v) + b χ (3)

3 Fg. 3. Lnear separablty Fg. 4. Non-Lnear separablty Commonly used kernels are lnear, polynomal and Radal Bass Functon (RBF) defned by Eq. 4. γ * x - y (x, y) = exp (4) Another mportant kernel s the sgmod functon defned by Equaton 5. T (x, y) = tanh γx y + r (5) ( ) The related SVM classfcaton s parameterzed by the penalty factor C and the kernel parameters. V. PROPOSED CLASSIFICATION METHOD Multspectral data classfcaton s based on a nonsupervsed segmentaton and then classfcaton by learnng method. Fg. 6 detals the process algorthm. For the learnng data base there are: Supervsed classfcaton for satellte observatons. Regon Feature extracton for classfed mage Fg. 5. Feature extracton and classfcaton. To classfy a multspectral mage, the test satellte scene wll be segmented nto homogenous regons. The learnng System Vector machne wll produce a separaton model based on the SVM learnng prncple [6]. The proposed method s based on the feature fuson level for regons. Spot mage are segmented nto regons by the watershed algorthm. Ths algorthm decomposes remote sensng mages by three steps: Derve varance mage from each channel mage. Varance wthn each pxel wndow s evaluated and assgned to the central pxel. The obtaned mage s the surface mage. In the derved surface mage, pxels values are treated as elevaton. Pxels wll be teratvely merged nto one watershed f they have closest elevaton. Mergng adjacent watersheds accordng to spectral smlarty. The Feature extracton wll produce a feature vector that wll be classfed by the SVM. For each regon R the feature vector s the concatenaton of the feature sub-vector from each band mage as shown n Eq. 6. For 4 band mages and M features we obtan the followng feature vector for the regon R : R =[F, F,., F M, F, F,., F M, F 3, F 3,., F 3M, F 4, F 4,., F 4M ] (6) Although the band correlaton ssue, t was shown that the best segmentaton and classfcaton results are gven by usng all band nformaton's. Wthn ths work, the feature space s deduced from all bands. Feature components are from Haar wavelet transform and Gabor wavelet [7]. Haar descrptors are computed n three drectons: horzontal, vertcal and oblque [8]. The choce of wavelet type and decomposton level s guded by experments. Many land covers of the studed zone have textural appearance lke parcels, urban areas, bare sol and mountan. Thus usng Gabor flters was sutable for the scene classfcaton. The next step s the regon classfcaton based on statc learnng from a set of recognzed regons. Fg. 7 contans some learnng database patches for some classes. Many SVM mplementatons exst. LIBSVM [9] gves an easy and relable tool for SVM applcaton. Learnng database s a part of the studed zone and contans many class patches. Scalng feature vectors to [0 ] s a determnatve step to optmze computatonal tme. Both learnng and test feature vectors are scaled wth the same factor. Fg. 8 s the test mage for the proposed classfcaton method. The mage s frst segmented by watershed algorthm (Fg. 9 a) and then classfed by the SVM method (Fg. 9 b) basng on feature vectors extracted from each regon. Table gves the classfcaton accuracy for dfferent kernels and parameters. The best accuracy s 88.35% and s reached by sgmod kernel. SVM parameters ncludng penalty classfcaton parameter C and sgmod parameter γ are determned by cross valdaton tests. Confuson matrx s detaled n Table. Lake, parcels and urban areas are well recognzed due to ther partcular textural appearance. For heterogeneous regons such as scattered vegetaton, dense vegetaton, wetland and Bare sol the well

4 classfed rate s more than 83%. Bare sol s confused wth vegetaton classes due to the varous elements for theses classes ncludng sol. TABLE. CLASSIFICATION ACCURACY FOR DIFFERENT SVM ERNELS ernel C γ Accuracy lnear % Polynomal % RBF % Sgmod % TABLE 3. CONFUSION MATRIX FOR SIGMOID ERNEL Lake Scattered Parcels Dense Wetland Bare Urban Vegetaton Vegetaton Sol areas Lake 00% 0% 0% 0% 0% 0% 0% Scattered 0% 87.43% 0%.4% 0% 8.03%.40% Vegetaton Parcels 0% 0% 97.50% % 0.5% 0% 0% Dense 0% 0% 3% 87% 0% 0% 0% Vegetaton Wetland 8.7% 0% 0.7% % 88.6% 0% 0% Bare Sol 0% 3.07% 0% 3.% 0% 83.33% 0.50% Urban areas 0% 0%.33% 0%.5% 0% 96.7% Classfcaton wt non-supervsed classfer lke k-means algorthm gves an accuracy of 5,7%. The classfed mage s presented n Fg. 0 (a). The supervsed classfcaton by Mnmum-Dstance algorthm gves 85,0% for the accuracy. Moreover the classfed mage shown n Fg. 0 (b) has many mss-classfed classes and heterogeneous zones. Fg. 6. Learnng data base patches. (a) (b) (c) (d) Fg. 7. Test mage of SPOT-4, May 3, 998. (a) Band. (b) Band. (c) Band 3. (d) Band 4. (a) (b) Fg. 0. Test mage classfed by k-means algorthm (fgure a) and by Mnmum Dstance algorhtm (fgure b) (a) (b) Fg. 8. Test mage segmentaton by Watershed algorthm (fgure a) and SVM classfcaton (fgure b) Fg.. Classfcaton method comparson by class

5 To compare n depth the classfcaton accuracy by class, Fg. provdes the good classfcaton rat by class for the proposed approach based on SVM and -means and Mnmum-dstance algorthms. Lake s well classfed n the three cases. Dense vegetaton class s better recognsed by Mnmum Dstance and -means method.. Reman classes are better recognsed by the proposed method Therefore, compared to classcal classfcaton method that deals wth pxel radances, the proposed approach provdes better accuracy and avod solated pxels. VI. CONCLUSION Wthn ths paper we have establshed a new classfcaton method for multspectral data. The proposed method s based on supervsed classfcaton and feature extracton for learnng data. Test satellte mage wll go throw the segmentaton process, feature extracton and then SVM classfcaton.. Ths work ams to fnd a relable classfcaton method for remote sensng data. Feature descrptors and learnng classfcaton consttute a sutable soluton for correlated data and for nonlnear classfcaton problem. The proposed approach could be appled to hyperspectral data and could be amelorated wth other segmentaton, classfcaton and feature extracton algorthms ACNOWLEDGMENT Ths work has been fnancally supported by the Mnstry of Hgher Educaton and Scentfc Research n Tunsa. REFERENCES [] V. S. V. Dhavale, "DWT and DCT based Robust Irs Feature Extracton and Recognton Algorthm for Bometrc Personal Identfcaton", Internatonal Journal of Computer Applcatons, Volume 40 No.7, p , Feb. 0. [] J. T. Hwang,. T. Chang and H. C. Chang, "Satellte mage classfcaton based on Gabor texture features and SVM", Internatonal Conference on GeoInformatcs, June 0. [3] E. Waltz and T. Waltz. Chapter 5: The prncples and practce of mage and spatal data fuson. In :Handbook of multsensor data fuson, Ed. par James Llnas Davs L. Hall, CRC press, p. 89-, 008. [4] A. Martn, G. Sevellec and I. Leblond,"Characterstcs vs decson fuson for sea-bottom characterzaton", Colloque Caractersaton nstu des fonds marns, Brest, France, 004. [5] W. Qang, "Classfcaton and Regularzaton n Learnng Theory", Doctor of phlosophy thess, Cty unversty of Hong ong, May 005. [6] A. Davd and, B. Lerner, "Support vector machne-based mage classfcaton for genetc syndrome dagnoss", Pattern Recognton Letters, Volume 6, p , June 005. [7]. E. Moorgas, "Image Compresson Preprocessng for ANN Ensemble Moton Detecton System", Proceedngs of the World Congress on Engneerng, Volume I, p , 00. [8]. H. Talukder and. Harada, "Haar Wavelet Based Approach for Image Compresson and Qualty Assessment of Compressed Image", Internatonal Journal of Appled Mathematcs, Volume 37, 007. [9] C. C. Chang, and C. J. Ln, LIBSVM: a lbrary for support vector machnes, 00. Soft-ware avalable at