IMAGE FUSION BASED ON EXTENSIONS OF INDEPENDENT COMPONENT ANALYSIS

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1 IMAGE FUSION BASED ON EXTENSIONS OF INDEPENDENT COMPONENT ANALYSIS M Chen a, *, Yngchun Fu b, Deren L c, Qanqng Qn c a College of Educaton Technology, Captal Normal Unversty, Bejng 00037,Chna - (merc@hotmal.com) b College of Geography Scence, South Chna Normal Unversty, Guangzhou, 5063,Chna - (fyc6@63.com) c State Key Laboratory of Informaton Engneerng n Surveyng,Mappng and Remote Sensng,Wuhan Unversty,9 Luoyu Road,Wuhan, Chna, (drl@whu.edu.cn,qqqn@lmars.whu.edu) Commsson VII, WG VII/6 KEY WORDS: mage processng, ntegraton, mage understandng, fuson, land cover ABSTRACT: Remote sensng mage fuson can effectvely mprove the accuracy of mage classfcaton. Ths paper proposes an mage fuson algorthm based on extensons of ndependent component analyss (ICA) and mult-classfer system. Frstly a novel method of fusng panchromatc and mult-spectral remote sensng mages s developed by contourlet transform whch can offer a much rcher set of drectons and shapes than wavelet. As ICA not only can effectvely remove the correlaton of mult-spectral mages, but also can realze sparse codng of mages and capture the essental edge structures and textures of mages, then usng features extracted from the extenson of ICA doman coeffcents of the fused mage, dfferent classfers correspondng to dfferent mage features are chosen n parallel style and the support vector machnes are traned to classfy the whole fused mage as stack part n the proposed mult-classfer system. Expermental results show that the proposed algorthm can effectvely mprove the accuracy of mage classfcaton.. INTRODUCTION Image fuson has receved sgnfcant attenton n remote sensng. It can be defned as the process of combnng two or more source mages from the same scene nto a composte mage wth extend nformaton content by usng a certan algorthms. The fused mage may provde ncreased nterpretaton capabltes and more relable results snce data wth dfferent characterstcs. The process of mage nformaton fuson can be performed at sgnal, feature, and symbol levels dependng on the representaton format at whch mage nformaton s processed. (Ranchn, 003). The objectve of mage fuson s to mprove the accuracy of the objectve recognton and classfcaton, whch can support the decson makng. The exstng research results show that by fusng the panchromatc and mult-spectral mages to gan the hgh spatal resoluton mult-spectral fuson remote sensng mages can effectvely mprove the accuracy of mage classfcaton. Besdes fusng dfferent classfcaton results from dfferent sngle feature sources at decson level can also be an effectve way to mprove the classfcaton results. Ths paper proposes an mage fuson algorthm of remote sensng mages based on extensons of ndependent component analyss (ICA) and mult-classfer system. Frstly a novel method of fusng panchromatc and mult-spectral remote sensng mages s developed by contourlet transform. Then usng dfferent features extracted from the extenson of ICA doman coeffcents of the fused mages, a parallel and stack mult-feature and mult-classfer decson level mage fuson algorthm s presented. The remander of the paper s organzed as follows. Secton recalls the concept of contourlet transform. Secton 3 ntroduces the foundatons and extensons of ICA. Secton 4 hghlghts the algorthm of the decson fuson algorthm based on extenson of ICA and mult-feature and mult-classfer system. Experments results and comparsons are presented and dscussed n Secton 5. Conclusons are drawn n Secton 6.. CONTOURLET TRANSFORM The contourlet transform (M.N.Do, 00) s an extenson of the wavelet transformaton n two dmensons usng mult-scale and drectonal flter banks. The contourlet expanson of mages conssts of bass mages orented at varous drectons n multple scales, wth flexble aspect ratos. Thus the contourlet transform not only retans the mult-scale and tme-frequency localzaton propertes of wavelets, but also t offers a hgh degree of drectonalty and ansotropy. The contoulet transform s mplemented n two stages: the subband decomposton stage and the drectonal decomposton stage. Recently developed contourlet transform can offer a much rcher set of drectons and shapes, and thus t s more effectve than wavelet n capturng smooth contours and geometrc structures n mages. Ths paper proposes a novel method of fusng panchromatc and mult-spectral remote sensng mages based on contourlet transform. * Correspondng author.

2 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B7. Bejng EXTENSION OF ICA 3. Independent Component Analyss Independent component analyss (ICA) (Hyvarnen,999) s a newly developed lnear data analyss method to separate blnd sources, whch has been used n some challengng felds of medcal sgnals analyss, features extracton and pattern recognton. The ICA model s defned as: x = A s () Where x s observed random vector, s s source random vector. The ICA soluton for the unmxng problem s to fnd a lnear transformaton W of dependent sensor sgnals x, that makes the ouputs y as ndependent as possble,.e. 3. Extenson of Independent Component Analyss 3.. Topographc Independent Component Analyss: As the estmated ndependent components by standard ICA are not completely ndependent, the resdual dependence structure could be used to defne a topographc order for the components. Topographc Independent Component Analyss (TICA) s a well-known ICA-based technque, whch uses the topographc order representaton to combnes topographc mappng wth ICA. In contrast to ICA, the components s are no longer ndependent but mutually energy-correlated accordng to the two-layer generatve model (Hyvarnen,00). TICA assumes that the varances of sources are dependent on each other through neghborhood functons. Ths dea leads to the followng representaton of the source sgnals: s = σ z (5) y = W x () Where y s an estmate of sources. The man task of ICA s to solve the separaton matrx W, the key of algorthms s to choose the method that measure the ndependence between sgnals. A large amount of algorthms have been developed for performng ICA. One of the best methods s the fxed pont FastICA algorthm. In the FastICA algorthm, negentropy s used as the crteron to estmate y as t s a natural measure of the ndependent between random varables. The goal s to maxmze ther negentropy. In the FastICA algorthm, the negentropy s approxmated by usng the contrast functon whch has the followng form: Ng(y) = {E[G(y)] E[G(ygauss)]} (3) Where y s random vector, y gauss s a standardzed gaussan varable. E [ ] s mathematcs expectaton, G [ ] s a nonquadratc functon. Here we choose: u G(u) = exp( ) (4) Where z s a random varable havng the same dstrbuton as s, and the varance σ s fxed to unty. The varance σ s further modeled by nonlnearty: n σ = φ( h(, k)u ) (6) k= k Where u are the hgher order ndependent components used to generate the varances, h(, j) s a neghborhood functon, and φ descrbes some nonlnear. Then TICA s gven as the followng update equaton: w j T : = w + αe{z(w z)r } (7) j where E(u) s the expectaton operator, α s the stepsze, and r = T h(, k)g( h(k, j)(w z) ) (8) k j j The functon g s the dervatve of G, such as tanh etc. 3.. Improved Neghborhood Kernel Functon: The h(, j) neghbourhood functon expresses the proxmty - th j - th between the and components. It can be defned n the same ways as wth the self-organzng map. At the present tme only the most common square type neghbourhood functon are used n the standard TICA,.e.

3 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B7. Bejng 008, f j m h(, j) = (9) 0, otherwse The constant m defnes the wdth of the neghborhood. In fact from the vew of neurobology the feedback ntensty between the central cell and other neghbor cells has busness wth the dstance,so neghborhood should be the functon of dstance. Consderng the characterstcs of human vsual system and under our many experments, ths paper ntroduces the gaussan neghborhood seres kernel functon to express the dfferent topology of TICA, such as: h (w, w g j nm ) ( n) + ( j m) [ ] r = e (0) The new ntroduced neghborhood functons can obtan the mage bass wth obvously enhanced drectonalty, whch has advantages for the comng mage analyss task Modfed Learnng Rule: To resolve the separaton matrx W, the optmzaton problem can be nduced as follows: T mn J w) = E{ G( h(, j)( w z( t)) )} ( n = subject to w = () The Lagrange functon can be derved as: The nformaton that remote sensng mage represent s the reflectvty of dfferent objects n certan band. Each band of mult-spectral remote sensng mages can be consdered as the combnaton of reflectvty of the several ndependent land objects n certan law. Applyng ICA to mult-spectral remote sensng mages, we can obtan the ndependent component bands that concentrate the nformaton of specfc land objects, resultng n enhancng the degree of separaton of dfferent objects. For sngle band remote sensng mage, most mportant nformaton such as edge features, texture features are nearly correlatve wth hgh-order statstcs. Hgh-order statstcs reflect the mportant structure and phase feature of mage. Image analyss usng ICA/TICA wth hgh-order statstcs has partcular advantage, t can realze sparse codng, meanwhle, ICA/TICA s excellent edge flter (Zeng,005). When people observe mage, a seres mage patches are pcked up frstly and then the whole mage. Suppose each mage patch s denoted by x, whch can be regarded as a lnear combnaton of the base functon matrx A, ndependent component s s the statstc ndependent random vector, expressng the coeffcents that the correspondng bass act on mage,.e. x = N = a s, where A = ( a, a, L, an ),column vector a ( =,, L, N) denotes a group of N pxels bass mages. Through ICA resolves the separaton matrx W, one can get the coeffcents projected n ndependent component bass by y = Wx, whch express the mage features n ICA doman. Fgure. are bass matrx A, bass vectors have orentaton n space doman and localzaton n frequency doman, depct most of the edge features of mage. Fgure. llustrates the bass vectors obtaned by our mproved TICA, one can observe the spatal correlaton of bass ntroduced by topography, the bass offer a more comprehensve representaton compared to the general ICA model. L(w, λ) = E{G( n = T h(, j)(w z(t)) )} + λ( w ) () Fnally the batch learnng rule can derved as: w' = w η(e{g(y)z} E{g(y)y}w) (3) Where η s learnng rate, here the self-adaptve adjustment method s developed n ths paper. Fgure. ICA bass of natural mage data Through ntroducton of Lagrange operator to solve the optmzaton of TICA, the method has relgous deducton procedure and well property of convergence. In short, ths paper ntroduces the new topographc kernel functons to express the relatonshps between the ndependent components, whch can better satsfy the human vson system demand than the former model. Further more, the paper also gves the new optmzaton rule to realze the farther development of TICA. The proposed modfed TICA s more applcable n mage fuson. Fgure. Improved TICA bass of natural mage data 3

4 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B7. Bejng IMAGE FUSION ALGORITHM BASED ON EXTENSIONS OF ICA AND MULTI-CLASSIFIER SYSTEM Ths part ntroduces the decson level remote sensng mage fuson algorthm. Exstng studes have show that by fusng mult-spectral mages and panchromatc mage to get the hgh qualty fuson mages can mprove the accuracy of classfcaton than just usng sngle source mage. Ths paper uses the panchromatc mage, mult-spectral mages and the resultng fused mages for researchng objects, extractng the spectral features, texture features and the features n ICA and TICA transformaton doman, usng dfferent classfers to get the classfcaton results correspondng to dfferent features and applyng the method of mult-classfer system to obtan the fnal classfcaton of land cover from remote sensng mages. 4. Image Fuson Based on Contourlet Transform Coregster both mages and resample the mult-spectral mages to make ts pxel sze equal to that of the panchromatc mage n order to get perfectly superposable mages. Here only R/G/B three channels are consdered. The mages are frstly decomposed by contourlet transform, gettng low frequency and hgh frequency coeffcents n dfferent resolutons and dfferent drectons. Then combnng ~ atrous wavelet, the fuson procedure s choosng dfferent rules on partcular sets of contourlet coeffcents that correspond to hgh and low frequency bands. The hgh-frequency coeffcents of the panchromatc mage substtute all the hgh-frequency coeffcents n R/G/B three channels. The panchromatc mage s decomposed by ~ atrous wavelet, gettng a group of wavelet plane coeffcents, then addng these wavelet coeffcents to the low-frequency contourlet coeffcents, that s further extracts the detal nformaton of panchromatc mage for fusng applcaton. Fnal fused mages are obtaned by usng reversed contourlet transform. The proposed method can get more nformaton n the fused results and the spectral reservng character s qute well. The contourlet transform mage fuson method offers a desrable result to mprove spatal resoluton and nformaton of the fused mages, whch get ready for the next classfcaton procedure. 4. Image Feature Extracton 4.. Spectral Feature: The spectral features of multspectral mages are the most essental features, here the three channels of hgh spatal resoluton fused mages are consdered. Besdes the spectral features extracted n the orgnal R/G/B color space, other mage features can be gotten by transformng the mult-spectral fused mage nto dfferent color space. How to choose the sutable color space s an mportant factor for dfferent color space can defne dfferent useful spectral features. So there are two key ponts should be consdered n choosng color space, one s that the color space can present the rrelevant color features, the other s that the color space remans constant n dfferent llumnaton condtons. Based on the above consderatons, the paper chooses the Ohta color space(ohta,985) whch can be expressed as followng. I I I 3 = (R + G + B) / 3 = (R B) / = ( G R B) / 4 (4) The components n Ohta color space are rrelevant, so t can well apperceve the change of color n statstcal vew. Ohta color space s gotten by lnear transform of RGB color space, here I s the ntensty component, I and I 3 are the almost orthogonal color components. The feature vectors obtaned from Ohta color space are denoted by T here. 4.. Texture Feature of TICA bass: Snce the orgnal panchromatc mage has hgh spatal resoluton and low spectral resoluton, dfferent objects have the same gray value or the same object has dfferent gray values n the panchromatc mage sometmes. As the fused mages can have hgh spatal and spectral resoluton smultaneously, the fused color mages are transformed nto the grayscale mage named grayscale modulaton mage (GMI) here by specfc algorthm. GMI s useful nformaton source because ts spatal resoluton s smlar to the orgnal panchromatc mage and ts gray spectral values can reflect dfferent objects much better. So by applyng TICA to the GMI block by block n sldng wndow style, the texture features of TICA bass can be extracted. In order to uncover the underlyng structure of an mage, t s common practce n mage analyss to express an mage as the synthess of several other bass mages. These bases are chosen accordng to serve some specfc analyss tasks. The advantage of the TICA bass s that the estmated transform can be talored to the needs of the applcaton. A set of mages wth smlar content to the GMI s selected for tranng the desred bases. Then usng the TICA bass the GMI s transformed nto the TICA doman n sldng N N wndow style. All of these extracted TICA features can well reflect the structure and texture nformaton of the fused mages. The TICA feature vectors are denoted by T here Independent Component Feature: The exstng studes show that the correlaton between the bands of multspectral mages sometmes brngs ll effect n mage classfcaton. ICA not only can remove the correlaton n the bands of mult-spectral mages, but also can makes the resultng components mutual ndependent as much as possble. The every resultng band of ndependent component embodes a concentrated reflecton of certan ground objects, ncreasng the degree of separaton between dfferent ground objects. Therefore ndependent component analyss can effectvely remove the unfavorable nfluence and rase the accuracy of classfcaton. Mult-spectral mages can be regarded as the lnear combnaton of mult-source mxture sgnals n some sense due to ther low spatal resoluton. Ths paper treats the panchromatc and multspectral mages as four dmensonal mxture sgnals and adopts ICA to obtan another fuson scheme of mult-spectral and panchromatc mages to get three dmensonal mult-spectral mages. Though the resultng ndependent components can not reserve the orgnal spectral characterstcs very well, they can express the sharpened mult-spectral mages and resolve the problem of unmxng the mxed mult-spectral mages pxels n the hypothess of lnear spectral mxture model, whch lay the foundaton for the next step of properly classfcaton. The 4

5 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B7. Bejng 008 extracted three ndependent component features are recorded as T3 here. By means of the methods of feature extracton mentoned above, ths paper can get three major feature of fused mages,.e. spectral feature (T), texture feature of TICA (T) and lnear transform feature of ICA (T3). 4.3 Mult-Classfer Constructon 4.3. Prncple of Mult-Classfer System: Classfcaton s the process of assgnng presented nformaton nto classes and categores of the same type. The classfcaton of the mage requres the estmaton of the posteror probablty for each class. Such estmates can be obtaned by usng supervsed and unsupervsed classfcaton algorthms. The output of a classfer can take abstract form, rank level and measurement level. In the past few years, sgnfcant efforts have been devoted to the development of effectve algorthms for combnng dfferent types of classfers n order to explot the complementary nformaton that they provde(burzzone,00; Ranawana,006). So f a multclassfer system s to be successful, the dfferent classfcaton should have good ndvdual performances and be suffcently dfferent from each other. A mult-classfer can be constructed ether n a parallel, stack or combned manner. Once the ndvdual classfers have been desgned and mplemented, the next most mportant task nvolves the combnaton of the ndvdual results obtaned through each ndvdual classfer. The strategy ncludes lnear combnaton methods, non-lnear combnaton methods, statstcal methods and computatonally ntellgent method. The success of a mult-classfer system depends on three key features: proper selecton of classfer wth dversty, topology and combnatonal methodology. The man purpose of multclassfer combnaton s to take advantage of the dfferent classfers to enhance the generalzaton ablty of the ndvdual classfer to gan the better results of classfcaton. Ths paper makes a useful attempt n the mult-classfer system and proposes a mult-classfer fuson method base on extenson of ICA Classfer Selecton: Correspondng to the three dfferent features n the fused mages, ths paper makes the ponted choce the followng classfers, ncludng K-NN classfer, BP neural network classfer, decson tree classfer and mult-category SVMs.. K-nearest neghbor classfer, K-NN. The K-NN has a very effectve strategy as a learner, t keeps all tranng nstances. A classfcaton s made by measurng the dstances from the test nstance to all tranng nstances, most commonly usng the Eucldean dstance. From these dstances, a dstance matrx s constructed between all possble parngs of ponts. The data ponts, k-closest neghbors are then found by analyzng the dstance matrx. The k-closest data ponts are then analyzed to determne whch class label s the most common among the set. Fnally the majorty class among the K nearest nstances s assgned to the test nstance. K-NN classfer s denoted by C here.. BP neural network classfer. Back-propagatng network (BP network) s a type of neural network. When postve drecton spread, the mported model dsposes layer by layer by way of hdden unts from the nput layer and sent to the output layer, neural state of each layer only affects state of the next layer. If the expected output can not be obtaned n the output layer, so transfer to back propagaton, and let error sgnal back along the orgnal lnk pathway, the error sgnal can became least through amend the values of each nerve cell. Ths paper chooses the BP neural network wth one hdden layer and uses C denotes t. 3. Decson tree classfer. The decson tree classfer s a set of herarchcal rules whch are successvely appled to the nput data. Those rules are thresholds used to bnary splt the data nto two groups. Each node s such that the descendant nodes are purer n terms of classes. Decson tree rules are explct and allow for dentfcaton of features whch are relevant to dstngush specfc classes. Then the analyss s reduced to the most useful layers. The structure of the decson tree can also be reveal herarchcal and nonlnear relatonshps among nput layers. These relatonshps often result n a gven class beng descrbed by varous termnal nodes. Termnal nodes are the fnal decson, whch assgn a sample to certan class. Here decson tree classfer s denoted by C3. 4. Support vector machnes, SVMs. Support vector machnes (SVMs) s a knd of machne learnng based on statstcal learnng theory(vladmr,000). The basc dea of applyng SVMs to pattern classfcaton can be stated brefly as follows: frstly map the nput vectors nto one feature space, ether lnearly or non-lnearly, whch s relevant wth the selecton of the kernel functon. Then wth the feature space from the frst step construct a hyperplane whch separates two classes,.ths can be extended to mult-class. The commonly used four kernel functon n SVMs are: lnear functon, polynomal functon, radal bass functon, sgmod functon. SVMs have the mportant computatonal advantage that no nonconvex optmzaton s nvolved. Moreover, ts performance s related to the margn wth whch t separates the data. As a new classfcaton technque, SVMs outperforms many conventonal approaches n varous applcatons. Here SVMs classfer s denoted by C Strategy of Mult-Classfer Fuson: Correspondng to the three dfferent features extracted from the fused mages and the four dfferent selected classfers, ths paper constructs the parallel topology of mult-classfers frstly, detal descrptons are as followngs. Towards the spectral features T n Ohta color space, K-NN C and decson tree C3 are chosen and combned n parallel topology. All the feature vectors are put nto the two classfers and respectve classfcaton results are obtaned n parallel topology style. For texture features of TICA bass, the paper chooses K-NN C and BP neural network classfer C and combnes them n parallel style, resultng two respectve classfcaton results. In regard to ndependent component features T3, K-NN C s chosen to get the correspondng classfcaton results. A number of tranng area for dfferent classes are chosen n the study mages, followng the above methods to extract spectral and ICA/TICA mage features, then tranng all the chosen classfers and the traned classfers are applyng to classfy the whole fused mages every pxel. Through dfferent classfers the correspondng posteror probablty of dfferent 5

6 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B7. Bejng 008 classfcaton results are gotten, so decson level fuson of these classfcaton results s needed Decson Fuson Strategy: There are many strateges for combnng classfcaton results of each ndvdual classfer, of whch majorty votng prncple and Bayesan combnaton strategy are the most common used fuson method..majorty Votng Prncple: The majorty votng method selects the relevant class by pollng all the classfers to see whch class s the most popular. Whchever class gets the hghest vote s selected. Ths method s partcularly successful when the classfers nvolved output bnary votes..bayesan Combnaton Strategy: Bayesan combners are used to carred out the classfcaton accordng to the Bayes rule by selectng the class assocated wth the maxmum average probablty. 3.The proposed Fuson Strategy: Dfferent from the routne fuson strategy, ths paper adopts SVMs C4 to fuse the dfferent classfcaton results correspondng to dfferent mage features to get the fnal fuson decson. Each classfcaton results of respectve classfers serve as the nput feature vectors for tranng SVMs, whch can be regarded as stack mult-classfer fuson style and the contnuaton of the aforementoned parallel mult-classfer system. The total fuson topology s as fgure 3. Moreover the above common fuson rules are also used to get the classfcaton results correspondng to dfferent features. Spectra feature ( T) K-NN (C) values n Ohta color space correspond to the frst type of nput feature vectors,.e. T. The fused color mages are transformed nto the GMI. Then applyng TICA to the GMI wth pxels of block n sldng wndow style to get four coeffcents of TICA doman, meanwhle, the statstcal parameters, such as mean, standard devaton, average gradent are computed for the second type of nput feature vectors,.e. T. Turnng the orgnal mult-spectral mages nto pxels vectors and convertng the orgnal panchromatc mage nto pxels vector to form pxels nput vectors, applyng ICA to the whole nput vectors, three ndependent component bands are shown n Fgure.5 (a)~ (c). The results ndcate that the three ndependent components play good role n separate the water body, naked land and dry land. Meanwhle the resultng fused false color mages n Fgure.5(d) have hgher spatal resoluton compare to the orgnal mult-spectral mages. All these three ndependent components are chosen as feature vectors for classfcaton,.e. T3. The tranng areas for dfferent classes are chosen n the mages, followng the above methods to extract spectral and ICA/TICA mage features and tranng all the chosen classfers, the traned classfers are then applyng to classfy the whole fused mages every pxel. Selectng sutable SVMs kernel functon and parameter to tran mult-category SVMs wth the nput feature vectors of every category obtanng from the afore parallel classfers. The traned mult-category SVMs are applyng to classfy the whole fused mages to gan the classfcaton results. Ths paper chooses the radal bass kernel functon: Panchromatc,multspectral and fused mages Texture feature of TICA bass ( T) Independent component feature ( T3) BP (C) Decson Tree (C3) SVMs Classfc aton results Fgure 3. paradgm of mult-feature and mult-classfer fuson 5. EXPERIMENTAL RESULTS In ths paper, to llustrate the proposed fuson procedure wth an example, the data used for ths experment are SPOT panchromatc and Landsat TM 5/4/3 mult-spectral mages, wth the same sze of pxels. Fgure. 4(a) ~ 4(b) are the panchromatc mage and the correspondng mult-spectral mages. The expermental area can be classfed nto water body (ncludng rver, paddy feld), naked land (ncludng road, resdental area, brdge and other undeveloped fled) and dry land by human vsual nterpretaton. The fused mages usng contourlet transform to fuse SPOT panchromatc and TM multspectral mages are shown n Fgure.4(c). K ( x, x' ) = exp( x - x' /σ ),where σ =, c =00 The classfcaton results correspondng to dfferent fuson rules are shown as follows. To test the effect of the proposed algorthm, the common fuson rules and the proposed novel mult-feature and mult-classfer algorthm for classfcaton are showed n Fgure.6 (c) and (e). Besdes, the tradtonal mndstance method and the max-lkelhood method of remote sensng mage classfcaton results are showed n Fgure. 6(a) and (b).to evaluate the classfcaton results objectvely, the total classfcaton accuracy s employed to descrbe the classfcaton precson of the mages by computng the degree of confuson between the statstcal samples and the actual samples through samplng randomly n classfcaton results. Table s the comparson of total classfcaton accuracy accordng to dfferent classfcaton methods. The tranng area of water body, naked land and dry land are selected n the mages, every category has two block of 6 6 pxels tranng samples, theses samples of three component 6

7 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B7. Bejng 008 Fgure.4 Orgnal SPOT(a), TM5/4/3 mages(b) and fused mages by contourle transform(c) Waterbody Naked land Dry land Fgure.5 The three ndependent component bands(a,b,c) of the fused mage by ICA(d) Fgure. 6 classfcaton results usng dfferent methods((a)mn Dstance(b)Max Lkelhood(c)Majorty Votng(d)Bayesan Combnaton(e)Proposed Algorghm) Classfcaton algorthm Total accuracy(%) Mn Dstance 6.58 Max Lkelhood Majorty Votng Prncple Bayesan Combnaton Strategy 8.80 Proposed Algorthm 8. Table the comparson of total accuracy of classfcaton usng dfferent methods As ICA not only can effectvely remove the correlaton of mult-spectral mages, but also can realze sparse codng of mages and capture the essental edge structures and textures of mages, then usng features extracted from the extensons of ICA doman coeffcents of the mages, dfferent classfers correspondng to dfferent features are chosen n parallel multclassfer style and the SVMs as stack fuson style are traned to classfy the whole mages n the proposed mult-feature and mult-classfer system. Expermental results show that the proposed algorthm can effectvely mprove the accuracy of mage classfcaton. REFERENCE 6. CONCLUSION Remote sensng mage classfcaton s an mportant means for quantfed remote sensng mage analyss, and remote sensng mage fuson can effectvely mprove the accuracy of mage classfcaton. Ths paper proposes an mage fuson algorthm based on extenson of ICA and mult-classfer system. A novel method of fusng panchromatc and mult-spectral remote sensng mages s developed by contourlet transform that can offer a much rcher set of drectons and shapes than wavelet. Therry Ranchn, Bruno Aazz, Lucano Alparone, Stefano Baront, Lucen Wald,003. Image fuson the ARSIS concept and some successful mplementaton schemes, ISPRS Journal of Photogrammetry & Remote Sensng, 58 :4~8 M.N.Do,M.Vetterl,00.Contourlets: A Drectonal Multresoluton Image Representaton, IEEE ICIP:357~360 Aapo Hyvarnen,999. Survey on ndependent component analyss, Neural Computng Surveys, :94~8 7

8 The Internatonal Archves of the Photogrammetry, Remote Sensng and Spatal Informaton Scences. Vol. XXXVII. Part B7. Bejng 008 Aapo Hyvarnen, Patrk O. Hoyer and Mka Ink,00 Topographc Independent Component Analyss. Neural Computaton, 3(7):57~558 Xangyan Zeng, Yenwe Chen, Deborah van Alphen, etc.005.selecton of ICA feature for texture classfcaton. ISNN, LNCS(3497):6~67 Ohta,Y.985.Knowledge-Based Interpretaton of Outdoor Natural Color Scenes. Research Notes n Artfcal Intellgence 4, Ptman Advanced Publshng Program Burzzone L, Cossu R,000. Combnng Parametrc and Nonparametrc classfers for an unsupervsed updatng of land cover maps. Proc. Of frst Internatonal workshop on multple classfer systems, Sprnger Romesh Ranawana, Vasle Palade,006.Mult-classfer systems: revew and roadmap for developers, Internatonal journal of hybrd ntellgent system,3:35~6 Vladmr N. Vapnk,000. The nature of statstcal learnng theory, Sprnger-Verlag Press, New York. 8