CLASSIFICATION of hyperspectral images (HSIs) has. Extinction Profiles Fusion for Hyperspectral Images Classification

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1 1 Extncton Profles Fuson for Hyperspectral Images Classfcaton Leyuan Fang, Senor Member, IEEE, Nanjun He, Student Member, IEEE, Shutao L, Senor Member, IEEE, Pedram Ghams, Member, IEEE, and Jón Atl Benedktsson, Fellow, IEEE Abstract Extncton profle (EP) s an effectve spatal-spectral feature extracton method for hyperspectral mages (HSIs), whch has recently drawn much attenton. However, the exstng methods utlze the EPs n a stackng way, whch s hard to fully explore the nformaton n EPs for HSI classfcaton. In ths paper, a novel fuson framework termed EPs-fuson (EPs-F) s proposed to explot the nformaton wthn and among EPs for HSI classfcaton. In general, EPs-F ncludes the followng two stages: In the frst stage, by extractng the EPs from three ndependent components (ICs) of a HSI, three complementary groups of EPs can be constructed. For each EP, an adaptve superpxel-based composte kernel strategy s proposed to explore the spatal nformaton wthn EP. The weghts to create the composte kernel and the number of superpxels are automatcally determned based on the spatal nformaton of each EP. In the second stage, snce the dfferent EPs contan hghly complementary nformaton, a smple yet effectve decson fuson method s further appled to obtan the fnal classfcaton result. Experments on three real HSI data sets verfy the qualtatve and quanttatve superorty of the proposed EPs-F method over several state-of-the-art HSI classfers. Index Terms Classfcaton, hyperspectral mage (HSI), extncton profle (EP), composte kernel, decson fuson. I. INTRODUCTION CLASSIFICATION of hyperspectral mages (HSIs) has many applcatons, ncludng the precson agrculture [1], urban mappng [2], and natonal defense [3]. In HSIs, each pxel s a hgh-dmensonal vector, and ts entres represent the spectral responses of dfferent spectral bands, whch ncorporate abundant spectral nformaton to acheve hgh classfcaton accuracy. In recent years, a number of classfers have been ntroduced for HSI classfcaton, ncludng the neural networks [4], multple kernel methods [5] [7], collaborate representaton [8], [9], and sparse representaton [10] [13], snce they are wdely used n many applcatons [14], [15]. Among these methods, Ths paper was supported by the Natonal Natural Scence Fund of Chna for Dstngushed Young Scholars under Grant , the Natonal Natural Scence Fund of Chna for Internatonal Cooperaton and Exchanges under Grant , the Natonal Natural Scence Foundaton under Grant No , and the Fund of Hunan Provnce for Scence and Technology Plan Project under Grant 2017RS3024. L. Fang, N. He and S. L are wth the College of Electrcal and Informaton Engneerng, Hunan Unversty, Changsha, , Chna (e-mal: fangleyuan@gmal.com; henanjun@hnu.edu.cn; shutao l@hnu.edu.cn). P. Ghams s wth the German Aerospace Center (DLR), Remote Sensng Technology Insttute (IMF), and Technsche Unverstät München, Sgnal Processng n Earth Observaton, Munch, Germany (e-mal: pedram.ghams@dlr.de). J. A. Benedktsson s wth the Faculty of Electrcal and Computer Engneerng, Unversty of Iceland, 101 Reykjavk, Iceland (e-mal: benedkt@h.s). support vector machnes (SVMs) [16], [17] have shown good performance especally when a lmted number of tranng samples are avalable [18]. In general, the tradtonal SVM method classfes the mage wthout consderng the contextual nformaton,.e., nterpxel dependency. Therefore, the classfcaton map obtaned by the SVM method may contan much salt and pepper nose. To enhance the classfcaton performance, the composte kernel-based SVMs were utlzed to combne both the spectral and spatal nformaton [19] [21]. In [19], Camps-Valls et al. constructed a famly of composte kernels by utlzng the Mercer s kernels, whch can explore the spatal nformaton n an effcent way. Moreover, n [20], a generalzed composte kernel framework was proposed to combne the spectral and spatal nformaton by a kernel stackng manner. On the other hand, varous feature extracton-based classfcaton methods have been proposed [22] [25]. In [23], the prncpal component analyss (PCA) was utlzed for feature extracton. In [24], [25], the ndependent component analyss (ICA) and the lnear dscrmnatve analyss (LDA) have been nvestgated for dscrmnatve feature extracton. In general, these methods are only desgned for spectral feature extracton, wthout consderng the spatal dependences of adjacent pxels. To enhance the classfcaton performance, spatal nformaton should also be taken nto account. A consderable number of spatal-spectral feature extracton-based classfcaton methods have been developed n the lteratures, ncludng the classcal Markov random feld (MRF) modelngbased methods [26], [27] morphologcal profles (MPs)-based methods [28], ts extended versons [29], [30], morphologcal attrbute profle (AP) [31], and extncton profle (EP) [32], [33]. Among these feature extracton methods, the EP has recently drawn much attenton snce t has followng advantages. Frstly, t can smultaneously preserve the geometrcal characterstcs of the nput mage, whle removeng unmportant detals. Secondly, t can delver a better recognton performance than the tradtonal spatal-spectral AP [32] feature extracton method. To apply the EP method to the HSI, the ndependent component analyss (ICA) s used to extract a few nformatve features from the HSI and the three man ICs of the HSI are preserved as the base mages to produce EPs. Snce the ICA method ams at mnmzng the dependence between ts components [34], the obtaned three EPs (denoted by EP 1, EP 2 and EP 3 ) are relatvely ndependent to each other, whch can reflect the characterstcs of HSI from dfferent aspects and offer hghly complementary nformaton among

2 2 Fg. 1. The flowchart of the proposed EPs-F algorthm. them. The procedure of constructon of three complementary EPs are llustrated n the second lne of Fg. 1. As can be observed, snce the mportant geometrcal characterstcs of ICs from a HSI are well preserved, there exsts abundant spatal nformaton wthn each EP. In addton, as the EPs are produced from relatvely ndependent ICs respectvely, each EP has ts own dstnctve characterstcs whch can offer complementary nformaton among EPs. Both the spatalspectral nformaton wthn and among EPs can be utlzed to enhance the classfcaton performance. However, the exstng method smply stack all EPs together [33], whch can not fully explot these nformaton n EPs for HSI classfcaton. In ths paper, we propose fuson framework termed EPsF to explot the nformaton wthn and among the EPs for HSI classfcaton. Generally, the proposed EPs-F method has two stages. In the frst stage, to utlze the abundant spatal nformaton wthn EP, an adaptve superpxel-based composte kernel strategy s proposed to fuse two types of shape-adaptve spatal features of EP and the spectral feature of orgnal HSI. Compared to other smlar methods whch need to set weghts of dfferent kernels manually, an adaptve strategy s proposed to produce the composte kernel based on the spatal nformaton. Moreover, an automatc approach s proposed to determne the number of superpxels base on the contextual nformaton of the EP. In the second stage, snce dfferent EPs reflect HSI n dfferent aspects whch can provde complementary nformaton n correspondng classfcaton maps, a smple yet effectve decson fuson method s consdered to fuse the classfcaton maps to get the fnal classfcaton result. The man contrbutons of ths paper are lsted as bellows. 1) Dfferent from the prevous stackng strategy n [33], we propose an effectve fuson framework to explot the rch spatal nformaton wthn EP and hghly complementary nformaton among EPs for HSI classfcaton. 2) To explore the spatal nformaton wthn EP, an adaptve superpxel-based composte kernel method s proposed, whch can not only adaptvely fuse the kernels of dfferent features, but also automatcally select the number of superpxels. The rest of ths paper s organzed as follows. In Secton II, we brefly revew the SVM method wth composte kernel for HSI classfcaton, extncton flters and EP-based feature extracton method. The proposed EPs-F method s detaled n Secton III. In Secton IV, the expermental results compared wth several state-of-the-art classfcaton methods are gven. Fnally, n Secton V, the paper s summarzed and the future works are suggested. II. R EVIEWS OF R ELATED M ETHODS In ths secton, we brefly revew the SVM method wth composte kernel for HSI classfcaton, the extncton flters (EFs), and the extncton profle (EP) feature extracton method. A. The SVM Method wth Composte Kernel Gven a labeled tranng data sets {(x1, y1 ) (xn, yn )}, where x RN and y { 1, +1}, and a nonlnear mappng

3 3 Fg. 2. The procedure for creatng shape adaptve regons = 1, 2, 3. φ( ), the SVM ams to solve the followng classfcaton problem: { 1 mn w,ξ,b 2 w 2 + C } ξ, subject to (1) y ( φ(x ), w + b) 1 ξ, = 1,, n ξ > 0, = 1,, n, where w and b defne a lnear classfer n the feature space. C s the regularzaton parameter to control the generalzaton capabltes of the classfer, and ξ are postve slack varables to cope wth the outlers n tranng samples. The kernel trck s often adopted n the SVM wth kernel functon defned as follows. K(x, x j ) = φ(x ), φ(x j ). (2) By ncorporatng equaton (2) nto (1), the fnal decson functon for the test x can be obtaned by solvng ts dual Lagrangan problem, whch shows as bellows: f(x) = sgn( n y α K(x, x) + b), (3) =1 where α are the Lagrange multplers. For HSI classfcaton, the one-aganst-one multclassfcaton scheme s adopted n the SVM method [19]. To further explot the spatal nformaton n HSI, the composte kernel strategy s adopted. Assume the orgnal tranng spectral pxels and spatal features are (x spe 1,, x spe n ) and (x spa 1,, x spa n ), respectvely, then two types of kernels can be constructed (.e., spectral kernel and spatal kernel denoted by K spe (x spe, x spe j ) and K spa (x spa, x spa j ) [19], respectvely). The composte kernel s computed by a weghed average operaton: K CW (x, x j ) = µ spe K spe (x spe, x spe j )+ µ spa K spa (x spa, x spa j ), where µ spe and µ spa are the weghts for the spectral kernel K spe (x spe, x spe j ) and the spatal kernel K spa (x spa, x spa j ), respectvely, and µ spe + µ spa = 1. The composte kernel s ntroduced nto equaton (3) to create a new decson rule for classfcaton. Compared wth a sngle kernel, snce the spatal and spectral nformaton of HSI are jontly consdered, the composte kernel can provde a better classfcaton performance. Among varous kernel functons, the radal bass functon (RBF) kernel s wdely used, whch s computed as: (4) K (x, x j ) = exp( x x j 2 /2σ 2 ). (5) B. Extncton Flters (EFs) The EFs are connected flters, whch preserve the relevant mage extrema. Relevance s measured by the concept of extncton values defned n [36]. The EFs are defned as follows: Let max(f) = {M 1, M 2,, M N } denote the regonal maxma of mage F. Each regonal maxma M has an extncton value ε correspondng to the ncreasng attrbute beng analyzed. The EF of F preserves the n maxma wth the hghest extncton values,.e., EF n (F), whch s gven as follows: EF n (F) = R δ F(G), (6) where RF δ (G) s the reconstructon by dlaton [37] of the mask mage G, whch s gven by G = max n =1 (M ). The max s the pxel-wse maxmum operaton. M 1 s the maxmum wth the hghest extncton value, and M 2 has the second hghest extncton value. C. The Extncton Profle The EP s composed of a sequence of thnnng and thckenng transformatons obtaned by a set of EFs wth a sequence of progressvely strcter crtera. For nstance, an EP for the nput gray-scale mage F can be defned as follows: EP(F) = φp λ,l (F), φ P λ,l 1 (F),, φ P λ,1 (F), F, }{{} thckenng profle γ P λ,l (F), γ P λ,l 1 (F),, γ P λ,1 (F) }{{}, thnnng profle wth P λl : {P λ } ( = 1,, L), a set of L ordered predcates (.e., P λ P λk, k) [32]. In order to extract the EP from a HSI, the ICA, as mentoned above, s frst utlzed to obtan the most nformatve components and the three ICs are used as base mages to produce EPs. Then, the EP method s appled on each IC as follows: EP 1 = EP(IC 1 ), EP 2 = EP(IC 2 ), EP 3 = EP(IC 3 ). Snce the ICs are relatvely ndependent to each other [34], the obtaned ICs can characterze the orgnal HSI n dfferent aspects whch can offer dstnctve and complementary nformaton n correspondng EP. The whole procedure of EPs extracton from ICs are shown n the frst two lnes of Fg. 1. In ths way, three complementary EPs can be constructed. III. PROPOSED EPS-F METHOD The framework of the proposed EPs-F method s summarzed by the flowchart n Fg. 1 whch ncludes the followng (7)

4 4 two stages: 1) Wthn EP, an adaptve composte kernel strategy s proposed to fuse the spatal nformaton of EP and the spectral nformaton of HSI, and then the composte kernel s fed nto an SVM to obtan the frst stage classfcaton maps; and 2) Among EPs, snce the classfcaton maps from dfferent EPs contan complementary nformaton, the decson fuson s further adopted to fuse the classfcaton maps for obtanng the fnal classfcaton result. A. Stage I: Fuson Wthn EP The stage I conssts of the followng steps: 1) The constructon of shape adaptve regons; 2) Intra and nter shape adaptve spatal features extracton; and 3) An adaptve strategy for composte kernel computaton. Note that, to explore the abundant spatal nformaton wthn each EP, one possble way s to use the fxed wndow based manner. However, there are some demerts assocated wth the fxed wndow. For example, on the edge structural area, the fxed sze wndow mght nclude the pxels from dfferent classes, and thus, the spatal nformaton of EP cannot be suffcently utlzed. Recent work [35] has demonstrated that the superpxel can adjust the shape of the wndow based on the contextual nformaton whch can more effectvely explore the spatal nformaton. Moreover, snce the HSIs have more than one hundred spectral bands and some bands may have heavy nose or artfacts, drectly segmentng the whole HSIs wth the superpxel method [38] [40] wll create very hgh computatonal cost and also be nterfered by the nose exsted n the spectral bands. To address ths ssue, our method apples the prncpal component analyss (PCA) [44] on the HSI, and exact the frst three PCs as the based mages for superpxel segmentaton, whch can greatly reduce the computatonal cost and s robust to nose. Accordng to the aforementoned analyss, for each EP, an over segmentaton based superpxel method [35] s appled on the frst three PCs to create shape adaptve regons for spatal nformaton extracton. 1) Constructon of Shape Adaptve Regons The PCA [44] s frst used to reduce the feature bands of the EP and the frst three prncpal components are used as the base mages (.e., I base ), snce they contan the most mportant nformaton of the EP. Then an over segmentaton method [35] s utlzed to construct a 2D superpxel map on the base mage. Subsequently, the 2D superpxel map whch contans the poston ndexes of pxels wthn each superpxel s appled on the correspondng EP to extract the nonoverlappng 3D shape, t = 1,, S num, S num s the number of superpxels). The procedure for creatng of shape adaptve regons s llustrated n Fg. 2. As a result, we can construct many 3D shape adaptve regons for spatal feature extracton. More detals about the over segmentaton algorthm can be found n [35]. Note that, n the superpxel segmentaton part, how to choose the number of superpxels s a key problem. Most of the exstng methods [41] [43] use a manual way to determne the number of superpxels for dfferent mages whch are very tme consumng and are hard to be adaptable to other complex scenaros. To address ths problem, we propose an adaptve adaptve regons (denoted by Y shape t Fg. 3. The example of shape adaptve regon Y shape t adaptve regonsy shape t,1, Y shape t,2,..., Y shape t,8 and ts neghbor shape strategy to determne ths parameter by utlzng the contextual nformaton. Specfcally, the Canny flter [45] s utlzed to detect the structural texture of the base mage I base and the number of nonzero elements N f n fltered mages (denoted by I flter ) s compared wth the total number of pxels N b n I base to create the texture rato R by: R = N f N b. (8) The number of superpxels S num s computed by the predefned number of base superpxel S base and the texture rato R as follows: S num = S base R. (9) For dfferent EPs, the number of base superpxel s set to be fxed, whle the texture rato s automatcally calculated based on the textural structures of the EPs by equaton (8). Therefore, the superpxel number S num can also be adaptvely adjusted wth the texture rato R. In ths way, not only the number of parameter s reduced, but also the spatal characterstcs of dfferent features are taken nto consderaton. 2) Intra And Inter Shape Adaptve Spatal Features Extracton As can be seen from Fg. 2, each shape adaptve regon ncludes a group of neghbor pxels whch are represented by yt k, k = 1,, K. K s the number of EP pxels wthn one shape adaptve regon. For each shape adaptve regon, we can extract two types of spatal features n the followng two ways: a) Intra-shape adaptve spatal feature. To explore the spatal feature wthn a shape adaptve regon, we frst rcompute the average of EP pxels (yt k, k = 1,, K), whch s denoted by ȳ t. The average pxel ȳ t s assgned to each EP pxel n Y shape t. We perform the same operaton for all the shape adaptve regons. Therefore, a mean EP feature EP mean can be constructed by consttutng all the fltered shape adaptve regons. b) Inter-shape adaptve spatal feature. By consderng the neghborng shape-adaptve regons shares the same spatal nformaton, a weghted average strategy s appled on the neghborng shape adaptve regons (.e., Y shape t,v, v = 1,, J) of the Y shape t, where J s the number of ts neghborng Inter-shape adaptve regons. The example of shape adaptve regon Y shape t and ts neghborng shape adaptve regons are

5 5 Fg. 4. The comparson between classfcaton maps obtaned from fuson stage I and the reference map. (a), (b) and (c) are the classfcaton maps (.e., map 1, map 2 and map 3 ) obtaned from EP 1, EP 2 and EP 3 n fuson stage I, respectvely, and (d) s the reference map. represented n Fg. 3. The Inter-shape adaptve spatal feature s defned by equaton (10). Specfcally, the weghted average operaton s appled on the average pxels ȳ t,v, v = 1,, J, whch are the average of Y shape y weght t = t,v, J w t,v ȳ t,v, (10) v=1 where w t,v = exp( ȳt ȳt,v 2 2 /h) Sum. Sum s defned as J v=1 exp( ȳ t ȳ t,v 2 2 /h), and h s a predefned scalar. Moreover, the y weght t s assgned to all EP pxels n Y shape t and all shape adaptve regons are conducted the same operaton. In ths way, a weghted average EP feature EP weght can be created. 3) Adaptve Strategy for Composte Kernel Computaton Assumng a set of spectral pxels (denoted by x spe 1,, x spe N, N s the number of the tranng samples) are randomly (or manually) selected from the orgnal HSI (denoted by X spe ) as tranng samples. The poston ndexes of selected spectral pxels are then utlzed to extract the correspondng spatal pxels from EP mean (wth mean EP pxels denoted by y1 mean,, yn mean ) and EP weght (wth weght average EP pxels denoted by y weght 1,, y weght N ), respectvely. Thus, we can construct three types of feature tranng samples (denoted by x spec 1,, x spec N, y1 mean,, yn mean and y weght 1,, y weght, respectvely). After that, the obtaned three knds of tranng samples are used to create three dfferent kernels by the RBF kernel functon n (5),.e., K tran spec ( x spe exp( x spe K tran ntraep ( y mean exp( y mean ( KnterEP tran exp( y weght y weght N, x spe ) j = x spe 2 /2σ 2 ), j, yj mean ) = y mean 2 /2σ 2 ), j ), y weght j = y weght 2 /2σ 2 ). j (11) (12) (13) Instead of manually selectng the weghts of the above three kernels, an adaptve composte kernel strategy s proposed to adjust the weght automatcally based on the contextual nformaton of X spe, EP mean and EP weght. Specfcally, accordng to the complexness of structural texture of I base s, and I base w (.e., the base mage of X spec, EP mean and I base m EP weght, respectvely), three texture ratos (.e., R s, R m and R w ) can also be computed by equaton (8). The fltered mage s obtaned by the Canny flter on the correspondng base mage. Note that, the base mages are obtaned by applyng the PCA transform on the EP and only adoptng the frst three prncpal components. The obtaned texture ratos are set as the weght of the correspondng kernels,.e., K CW = R s norm Ktran spe R m ( x spe norm Ktran ntraep R w norm Ktran nterep, x spe j ( y mean ( y weght ) +, yj mean ) + ), y weght, j (14) where norm = R s + R m + R w. In ths way, the structural nformaton of X spe, EP mean and EP weght are well utlzed n the composte kernel weghts. Fnally, the obtaned composte kernel s ncorporated nto the SVM classfer to create the classfcaton map of the test samples. The aforementoned operatons are appled on each EP. Therefore, three complementary classfcaton maps can be obtaned whch are denoted by map 1, map 2 and map 3 (see the examples n Fg. 4). B. Stage II: Fuson among EPs Snce each EP has ts own dstnctve characterstcs whch can reflect the orgnal HSI n dfferent aspects, the classfcaton maps (.e., map 1, map 2 and map 3 ) obtaned from dfferent EPs have complementary nformaton. For example, from Fg. 4, we can observe that the classfcaton map 1 delvers the best classfcaton performance n the red crcle area, whereas map 2 and map 3 show some wrong classfcatons n dfferent parts of the red crcle area. The same stuaton can also be observed at other regons (e.g., the green crcle and the yellow crcle area). Thus, n ths stage, the decson fuson s further utlzed to fuse the three obtaned classfcaton maps for creatng the fnal classfcaton result. Specfcally, the majorty votng (MV) s adopted n the three obtaned classfcaton maps (map 1, map 2 and map 3 ) pxel by pxel. We count the number of each class occurrence and denote them as Count 1, Count 2,, Count M, where M s the class number. The class label of the test pxel can be obtaned by: L = max m (Count 1, Count 2,, Count M ). (15) In very small number of cases,.e., for few test samples, the three classfcaton map s labels are dfferent whch s hard to choose the rght map s label as the fnal result. To address ths ssue, we defne a confdence level wth ntaton value be zero for each map (.e., map 1, map 2 and map 3 ). When one makes majorty vote (.e., ts vote belongs to the majorty vote), the confdence level of the correspondng map wll ncrease by one. We fnally choose the labels of classfcaton map that has the hghest confdence level as the labels for those undetermned test samples. IV. EXPERIMENTAL RESULTS AND DISCUSSION The proposed EPs-F method s tested on three HSI mages,.e., the Arborne Vsble/Infrared Imagng Spectrometer

6 6 TABLE I NUMBERS OF SAMPLES IN DIFFERENT CLASSES IN THE THREE TEST IMAGES. Indan Pnes Unversty of Pava Houston Unversty Image Class Name Number Class Name Number Class Name Number Class Name Number 1 Corn-notll Asphalt Healthy grass Parkng Lot Corn-mntll Meadows Stressed grass Parkng Lot Grass-pasture Gravel Synthetc grass Tenns court Grass-trees Trees Tress Runnng track Hay-wndrowed Metal sheet Sol 1242 Total Sybean-notll Bare Sol Water Sybean-mntll Btumen Resdental Sybean-clean Brcks Commercal Woods Shadows Road Bldg-grass-trees 386 Total Hghway 382 Total Ralway 114 weghts n the SVM-CK were manually selected. The SC- MK method adopted a superpxel method to extract both the spatal and spectral nformaton. In the GCK-MLR method, a generalzed composte kernel whch fused the spectral-spatal nformaton was constructed for HSI classfcaton. For the MFNL method, varous features extracted from HSI were combned together for classfcaton. For the EPs-stackng method, the dfferent EPs are used n a stackng way whch s hard to fully explore the nformaton n EPs. To objectvely evaluate the classfcaton results, three metrcs of overall accuracy (OA), average accuracy (AA), and Kappa coeffcent (K) are adopted. Besdes, the average and standard devaton of the classfcaton accuraces over ten runs are reported. The code of the proposed EPs-F method wll be released on the webste 1. Fg. 5. The reference map and classfcaton results (%) for the Indan Pnes mage. (a) False-color composte mages, (b) Reference, (c) SVM [16], (d) EMP [29], (e) SVM-CK [19], (f) EPF [46], (g) GCK-MLR [20], (h) SC-MK [21], () MNFL [47], (j) EPs-stackng [33] and (k) the proposed EPs-F. (AVIRIS) Indan Pnes mage, the Reflectve Optcs System Imagng Spectrometer (ROSIS-03) Unversty of Pava mage, and the Houston Unversty mage. In addton, the classfcaton results of the EPs-F method on the three test mages are vsually and quanttatvely compared wth several wellknown HSI classfcaton methods (.e., the SVM [16], EMP [29], edge preservng flter (EPF) [46], SVM-composte kernel (SVM-CK) [19], generalzed composte kernel-based multvarate logstc regresson (GCK-MLR) [20], superpxel-based classfcaton va multple kernels (SC-MK) [21], multple nonlnear feature learnng wth multvarate logstc regresson (MFNL) [47], extncton profles wth a stackng manner (EPs-stackng) [33]). The SVM s a pxel-wse classfcaton method, whch does not consder spatal nformaton. For the EMP and EPF method, the spatal context of the HSI was exploted by the morphologcal method and edge-flterng, respectvely. In the SVM-CK method, the mean of the neghborhood pxels n a fxed wndow was utlzed to extract the spatal features, and then the spatal features were combned wth spectral features usng a weghted composte kernel. The A. Expermental Data Sets 1) AVIRIS Indan Pnes: The Indan Pnes mage was acqured by the AVIRIS sensor over the agrcultural Indan Pnes ste n northwestern Indana. The sze of ths mage s , where 20 water absorpton bands are removed. The spatal resoluton of the mage s 20m per pxel and the spectral coverage ranges from 200nm to 240nm. The reference map of ths mage contans sxteen classes, most of whch are dfferent knds of crops. In our experment, the major ten classes are used and the reference classes are reported n Table I. Fg. 5 (a) and (b) demonstrate the false-color composte of the Indan Pnes mage and the correspondng reference data. 2) Unversty of Pava: The Unversty of Pava mage was captured by the ROSIS-03 sensor over an urban area surroundng the Unversty of Pava, Italy. The ROSIS-03 sensor generated an mage wth a geometrc resoluton of 1.3m per pxel wth the spectral coverage rangng from 430nm to 860nm. Ths mage s of sze , where 12 spectral bands are removed due to the exstence of hgh nose. The reference of ths mage contans nne ground-truth classes and reference classes are represented n Table I. Fg. 6 (a) and (b) show the false-color composte of the Unversty of Pava mage and the correspondng reference data. 3) Houston Unversty: The Houston Unversty mage was acqured over the Houston Unversty campus and ts negh- 1

7 7 For the SVM-CK method, the summaton composte kernel s used to make a decson rule, n whch the weght of the spectral content and textural content are set as the optmal value n [21]. The parameters of the SVM classfer used n the SVM-CK method are also selected by the fve-fold cross-valdaton technque. The parameters of the EMP, EPF, GCK-MLR, MFNL, EPs-stackng and SC-MK are set as the defaulted values reported n [29], [46], [19], [47], [33] and [21], respectvely. C. Results Comparson Fg. 6. The reference map and classfcaton results (%) for the Unversty of Pava mage. (a) False-color composte mages, (b) Reference, (c) SVM [16], (d) EMP [29], (e) SVM-CK [19], (f) EPF [46], (g) GCK-MLR [20], (h) SC-MK [21], () MNFL [47], (j) EPs-stackng [33] and (k) the proposed EPs-F. borng area, whch was used n the 2013 GRSS Data Fuson Contest. The hyperspectral data contans 144 spectral bands n the 380nm-1050nm regon, and pxels wth a spatal resoluton of 2.5m. Ths mage s an urban data sets whose most of the land covers are man-made objects whch contans ffteen classes. The reference classes are also shown n Table I. Fg. 7 (a) and (b) show the false-color composte of the Houston Unversty mage and the correspondng reference data. B. Parameter Settng For the tested Indan Pnes, Unversty of Pava, and Houston Unversty mages, the number of base superpxels are set to 1750, 5000, and 45000, respectvely. The parameter h n (10) s set to 500. The effects of number of base superpxels and h are dscussed n the followng subsecton. The σ n (11)(13) s set to 1 and the parameters of the SVM classfer n our method are selected by a fve-fold cross-valdaton technque. The parameters of the other test methods are set as follows: The parameters of the SVM method are determned by the fve-fold cross-valdaton technque. Fg. 5 shows dfferent classfcaton maps obtaned by dfferent nvestgated methods on the Indan Pnes mage wth 50 tranng samples are randomly selected for each class (Tr=50). As can be observed, by only consderng the spectral nformaton, the SVM method shows a very nosy estmaton n ts classfcaton map. By ncorporatng the local spatal context of the HSI, the EMP, SVM-CK, EPF, and GCK-MLR delver a smoother vsual result. However, those approaches stll fal to dentfy the pxels n the detaled and edge regons (e.g., the 7th class, Soynbeans-mntll). In addton, by combnng varous spatal features (e.g., nonlnear and lnear features of HSI) or extractng the spatal nformaton of HSI wth a more sophstcated strategy, the MNFL and SC-MK methods have better vsual performance, but stll exstng some wrong classfcaton labels (e.g., the 4th class, area of grass tree). Snce the rch spatal nformaton n EPs can not be fully explored by a smple stackng manner, the EPs-stackng method also fals to classfy the pxels n some area (e.g., the 7th class, Sybeanmntll). By contrast, the proposed EPs-F method has the best vsual classfcaton performance, whch not only reduces the nose greatly, but also preserves the meanngful structural nformaton. The correspondng classfcaton accuraces are reported n Table II wth the best results n bold. As can be seen, the proposed method acheves the hghest classfcaton accuracy n terms of OA, AA and K. Fg. 6. llustrates dfferent classfcaton maps obtaned by dfferent test methods on the Unversty of Pava mage wth 50 tranng samples are randomly selected per class (Tr=50). As can be observed from Fg. 6, the SVM classfcaton map s stll very nosy. The EMP, SVM-CK and MNFL can delver a comparatvely smooth result, but stll fal to detect some meanngful regons (e.g., the detaled or near-edge areas). Although, the EPF, GCK-MLR and SC-MK algorthms show mprovements on the detecton of the detals, there s stll notceable salt and pepper nose n the large-scale green area (e.g., 2nd class, meadows, at the bottom of the classfcaton map). The same stuaton can also be observed on the area of the 6th class (Bare sol) of the classfcaton map obtaned by the EPs-stackng method. By contrast, the proposed EPsF method can provde the best vsual performance at these areas, wth great suppresson on nose. The correspondng quanttatve results are shown n Table III wth the best results n bold. As can be seen, compared wth the SVMCK, GCK-MLR, SC-MK, and MFNL methods, the average

8 8 Fg. 7. The reference map and classfcaton results (%) for the Houston Unversty mage. (a) False-color composte mages, (b) Reference, (c) SVM [16], (d) EMP [29], (e) SVM-CK [19], (f) EPF [46], (g) GCK-MLR [20], (h) SC-MK [21], () MNFL [47], (j) EPs-stackng [33] and (k) the proposed EPs-F. mprovements of the proposed method are over 2.1%, 1.7%, and 2.7%, n terms of OA, AA and K, respectvely. In addton, another experment s conducted on the Houston Unversty mage, wth dfferent numbers of randomly selected tranng samples (.e., 10, 30 and 50 tranng samples for each class, respectvely) to evaluate the performance of the nvestgated methods. The quanttatve results are represented n Table IV wth the best results n bold. As can be observed, the proposed EPs-F method outperforms all the compared methods n terms of OA, AA and K even f only a small number of tranng samples are avalable. The man reasons are two folds. On the one hand, the EP can well preserve the geometrcal characterstcs of HSI whch can offer a dscrmnatve nformaton for classfcaton. On the other hand, by fusng the abundant spatal nformaton wthn each EP (.e., Fuson Stage I) and ncorporatng the complementary nformaton among EPs (.e., Fuson Stage II), the dscrmnatve nformaton s suffcently utlzed. The vsual performance comparson of dfferent methods wth Tr = 50 are represented n Fg. 7. As can be seen, the proposed EPs-F method provdes the best vsual classfcaton result compared wth other test methods (see the zoom regons of Fg. 7). D. Effects of Fuson Stage I And Fuson Stage II Ths secton analyzes the effects of the Fuson Stages I and II n the proposed EPs-F algorthm. 1) Effect of Fuson Stage I: The step of spatal nformaton extracton wthn EP s removed and the three complementary EPs are drectly combned together to create a composte kernel for classfcaton whle other parameters and operatons reman unchanged. The correspondng classfcaton performance s compared wth the proposed EPs-F on varous Fg. 8. The classfcaton result (n OA, AA, and K) comparson between EPs-F and EPs-F wthout Fuson Stage I usng a varous number of tranng samples. (a) Indan Pnes, (b) Unversty of Pava, and (c) Houston Unversty mages. Fg. 9. Correlaton coeffcent matrces across dfferent EPs for the three test HSI mages. mages, whch s llustrated n Fg. 8. As can be observed, the proposed method delvers a better classfcaton performance on dfferent mages consderng varous numbers of tranng samples. Specfcally, for the Indan Pnes data sets, the classfcaton accuracy decreases when the Fuson Stage I s removed. When only 10 tranng samples are selected for each class (.e., Tr =10), the OA of the proposed EPs-F method s 83.35%, whle the OA of the EPs-F wthout stage I s 73.58% whch are shown n Table V wth the best results n bold. It can be seen that the OA decreases by almost 10%. The

9 9 man reason s that wthout the Fuson Stage I, the abundant spatal nformaton wthn each EP cannot be well exploted. The same stuaton can also be observed for the Unversty of Pava data sets and Houston Unversty data sets. Overall, the proposed fuson frameworks can consstently acheve the best results n terms of classfcaton accuraces when the spatal nformaton wthn EP are effectvely exploted. 2) Effect of Fuson Stage II: Frst, the complementary propertes between dfferent EPs are nvestgated. Fg.9 shows the correlaton coeffcent matrx across the dfferent EPs on the three test mages and the bar n the last column represents the correlaton value n coeffcent matrx. As can be observed, the correlatons wthn each EP s very strong, whle dfferent EPs have much smaller correlatons. These low correlatons among EPs just show the dfferences whle stll be complementary. Note that, gven a matrx X = [x 1, x 2,, x n ], ts correlaton coeffcent matrx R R n n s calculated by equaton (16). R(, j) = cov(x, x j ) cov(x, x ) cov(x j, x j ). (16) where cov denotes the covarance operaton. Then, each EP s regarded as an ndependent feature and only spatal nformaton wthn each EP s consdered to create the classfcaton result. The parameters and operaton are smlar to the EPs-F. Fg. 10 shows the classfcaton results comparson between EPs-F and EPs-F wthout Fuson Stage II on dfferent mages and number of tranng samples. As can be seen, by utlzng the complementary nformaton among dfferent EPs wth the MV, the classfcaton performance of the proposed EPs-F method outperforms other sngle features (.e., EP 1, EP 2 and EP 3 ) and the advantage of the proposed method becomes more obvous when the avalable tranng samples are lmted (e.g., Tr =10). For example, as shown n Table V, for the Unversty of Pava data sets, the proposed method has the hghest classfcaton accuracy wth OA = 92.56%, whereas the OA of other sngle features (.e., EP 1, EP 2 and EP 3 ) are 88.82%, 89.59% and 86.11% wth the decrease of 3.74%, 2.97% and 6.45%, respectvely. The same results can also be observed on the other two data sets (e.g., Indan Pnes and Houston Unversty), whch demonstrates the MV can utlze the complementary nformaton among dfferent EPs n our proposed fuson framework for classfcaton. Note that, the best results n Table V are shown n bold. E. Parameter Dscusson 1) Effects of Number of Base Superpxel: In ths part, the effect of number of base superpxel s nvestgated. The numbers of tranng and test samples are selected to be the same as n the aforementoned experments on the Indan Pnes, Unversty of Pava mages and Houston unversty mage. Accordng to the sze of the mage, the number of base superpxel for the Indan Pnes mage s selected from 500 to 5000 wth the step sze of 250. The number of base superpxel for the Unversty of Pava mage s selected from 1000 to wth the step sze of 1000, whle the number of base superpxel on the Houston Unversty mage s selected from Fg. 10. The classfcaton results (n OA, AA, and K) comparson between EPs-F and EPs-F wthout Fuson Stage II at varous number of tranng samples. (a) Indan Pnes, (b) Unversty of Pava, and (c) Houston Unversty mages. Fg. 11. The effects of number of base superpxel on three dfferent mages. (a) Indan Pnes (Tr = 50), (b) Unversty of Pava (Tr = 50), and (c) Houston Unversty mages (Tr = 10, Tr = 30 and Tr = 50) to wth the step sze of Fg. 11 llustrates the OA values of the proposed EPs-F method under dfferent base superpxel numbers on all three test mages. Specfcally, for Indan Pnes, as can be observed, the classfcaton accuracy on OA reaches the best when the superpxel number equals to 1750 (marked by a red crcle) and the OA decreases when the number of base superpxel s selected to be other values. Ths s manly due to the fact that when the number of base superpxel s less than 1750, the spatal nformaton wth dfferent classes mght be ncluded nto one superpxel, and thus, t leads to the decrease n the classfcaton accuracy. On the other hand, when the number of base superpxel s larger than 1750, the sze of each superpxel wll become small, and thus, the spatal nformaton (e.g., n large homogeneous regons of the Indan Pnes mage) wll not be suffcently exploted for classfcaton. In addton, the same stuaton can also be seen on other test two mages (.e., the Unversty of Pava and Houston Unversty) wth the optmal number of base superpxel are 5000 and 45000, respectvely. Note that, wth dfferent number of tranng samples, the best number of base superpxel for Houston unversty are same (see the (c) n Fg. 11) whch demonstrates the robustness of the proposed method. 2) Effect of Parameter h: The proposed method s tested on three test mages wth the h vared from 100 to 1000 (wth Tr = 50), whch s shown n Fg. 12. As can be observed, the classfcaton results on the three test mages reman comparatvely stable. For example, on the Indan Pnes mage, the dfference between the maxmal OA and mnmum OA s less than 0.5%. Moreover, snce the Unversty of Pava and Houston Unversty mage are acqured from the urban area wth more detaled structures, the optmal h for the Unversty of Pava and Houston Unversty mage are smaller than the h for Indan Pnes. The optmal h for the Indan Pnes, Unversty

10 10 TABLE II THE AVERAGE ACCURACY(STANDARD DEVIATION)(%) OF TEN REPEATED EXPERIMENTS ON INDIAN PINES IMAGE OBTAINED BY DIFFERENT METHODS WITH 50 TRAINING SAMPLES PER CLASS. Class SVM [16] EMP [29] EPF [46] SVM-CK [19] GCK-MLR [20] SC-MK [21] MNFL [47] EPs-stackng [33] EPs-F (3.69) 83.17(4.40) 84.47(6.02) 78.24(6.91) 78.70(3.97) 93.80(3.63) 91.13(2.80) 82.02(1.15) 90.71(3.29) (4.70) 84.47(5.48) 87.23(9.66) 84.15(3.58) 91.56(2.13) 95.41(1.83) 91.01(3.01) 83.85(7.07) 95.45(3.16) (3.76) 89.85(3.71) 97.09(2.04) 93.95(3.22) 92.21(2.04) 97.16(2.16) 92.20(1.98) 94.78(1.80) 98.45(0.76) (1.69) 97.18(0.86) 96.85(1.66) 98.47(1.80) 98.79(0.67) 99.85(0.01) 91.25(0.54) 98.06(0.94) 99.96(0.01) (1.71) 99.98(0.06) 98.70(1.84) 99.86(0.04) 99.98(0.06) 100(0) 92.81(0.25) 99.72(0.03) 100(0) (3.91) 84.03(5.08) 75.50(5.27) 77.53(2.36) 85.07(2.23) 94.99(3.74) 92.03(2.91) 85.03(3.44) 96.17(1.71) (2.41) 92.66(2.26) 95.93(2.86) 75.07(2.87) 85.38(2.10) 90.61(2.73) 93.49(1.71) 81.23(5.31) 93.67(3.78) (2.28) 73.51(1.94) 72.50(4.98) 79.89(2.79) 90.33(2.70) 96.28(1.75) 90.98(1.51) 83.61(6.03) 96.94(1.88) (0.91) 99.86(0.14) 99.43(0.38) 92.35(1.71) 98.35(0.95) 98.47(0.05) 91.74(1.60) 97.66(1.04) 99.79(0.01) (4.70) 95.32(2.15) 73.99(9.57) 94.40(3.10) 95.60(2.81) 98.42(0.60) 91.85(3.66) 97.62(1.97) 99.70(0.02) OA 72.73(0.85) 89.43(1.67) 88.31(1.80) 83.68(2.35) 89.36(0.70) 95.06(0.67) 90.86(0.80) 87.66(1.93) 95.85(0.46) AA 68.72(0.93) 87.76(1.90) 86.51(2.01) 87.39(1.81) 91.71(0.40) 96.50(0.40) 91.85(0.78) 90.43(1.47) 97.09(0.24) K 73.52(0.61) 90.01(1.62) 88.22(1.53) 81.14(2.64) 87.73(0.82) 94.32(0.81) 89.44(0.91) 85.89(2.21) 95.22(0.52) TABLE III THE AVERAGE ACCURACY (STANDARD DEVIATION)(%) OF TEN REPEATED EXPERIMENTS ON UNIVERSITY OF PAVIA IMAGE OBTAINED BY DIFFERENT METHODS WITH 50 TRAINING SAMPLES PER CLASS. Class SVM [16] EMP [29] EPF [46] SVM-CK [19] GCK-MLR [20] SC-MK [21] MNFL [47] EPs-stackng [33] EPs-F (3.69) 98.61(0.96) 98.00(0.60) 88.23(3.32) 95.68(0.68) 95.37(1.50) 95.92(1.11) 94.48(1.53) 98.03(0.72) (4.70) 98.62(1.05) 98.43(1.03) 92.60(3.83) 97.38(1.56) 95.62(1.35) 95.90(1.53) 95.79(1.89) 98.27(1.10) (3.76) 73.73(0.83) 92.18(7.45) 85.75(2.76) 91.91(2.81) 97.76(1.61) 84.76(3.50) 97.40(0.78) 99.39(0.07) (1.69) 96.09(1.01) 91.87(8.31) 94.15(1.78) 95.09(1.11) 96.34(1.21) 95.32(1.49) 98.79(0.89) 98.01(0.83) (1.71) 98.89(0.18) 97.55(3.11) 99.52(0.09) 99.21(0.21) 99.96(0.06) 99.49(0.24) 99.52(0.19) 99.94(0.04) (3.91) 85.79(4.22) 80.13(8.67) 91.46(2.16) 97.03(0.89) 97.78(1.66) 96.41(0.84) 96.65(0.66) 99.90(0.07) (2.41) 94.62(0.62) 86.99(7.29) 92.47(2.80) 97.86(0.97) 99.95(0.02) 98.99(0.68) 97.62(0.75) 99.96(0.05) (5.98) 92.31(1.45) 91.47(7.86) 83.39(4.21) 93.14(2.95) 94.84(2.74) 87.57(3.01) 97.16(1.76) 99.14(0.36) (0.02) 99.79(0.03) 98.91(0.71) 99.96(0.01) 99.77(0.14) 99.99(0.01) 99.83(0.11) 98.13(1.11) 99.99(0.06) OA 84.06(1.09) 94.57(0.77) 93.57(2.39) 91.14(1.06) 96.40(0.36) 96.28(1.48) 94.95(0.72) 96.76(0.59) 98.67(0.67) AA 79.35(1.26) 92.85(1.01) 91.60(3.03) 91.95(0.61) 96.34(0.31) 97.51(0.70) 94.91(0.36) 97.53(0.28) 99.18(0.18) K 82.02(0.91) 93.21(1.02) 92.82(2.55) 88.34(1.37) 95.24(0.51) 95.11(1.91) 93.43(0.91) 95.68(0.76) 98.23(0.93) TABLE IV THE AVERAGE ACCURACY (STANDARD DEVIATION)(%) OF TEN REPEATED EXPERIMENTS ON HOUSTON UNIVERSITY IMAGE WITH VARIOUS TRAINING SAMPLES OBTAINED BY DIFFERENT METHODS. Tr=10 Tr=30 Tr=50 Method OA AA K OA AA K OA AA K SVM [16] 74.05(2.07) 71.97(2.20) 75.21(0.16) 86.35(1.14) 85.24(1.22) 86.23(1.01) 89.93(0.63) 89.10(0.68) 89.83(0.72) EMP [29] 78.50(2.22) 76.79(2.38) 80.44(2.02) 88.03(0.58) 87.07(0.62) 88.62(0.73) 92.25(0.60) 91.62(0.65) 92.41(0.52) EPF [46] 78.73(2.42) 77.02(2.59) 78.22(2.02) 91.35(0.97) 90.65(1.04) 91.02(1.31) 94.55(0.80) 94.10(0.86) 94.41(1.02) SVM-CK [19] 77.04(2.03) 78.46(1.68) 75.19(2.17) 88.95(1.49) 89.62(1.56) 88.05(1.56) 92.27(0.42) 92.63(0.43) 91.04(0.46) GCK-MLR [20] 80.45(1.54) 81.72(1.20) 78.91(1.71) 91.66(1.07) 91.90(0.89) 91.01(1.12) 94.41(0.49) 94.45(0.50) 94.01(0.53) SC-MK [21] 74.84(3.19) 77.80(2.91) 72.32(3.44) 89.57(0.10) 90.58(0.85) 88.71(1.12) 92.44(0.73) 93.17(0.60) 91.82(0.08) MNFL [47] 69.79(2.91) 72.66(2.02) 67.28(2.01) 89.41(0.84) 89.87(0.63) 88.52(0.94) 93.38(0.36) 93.46(0.34) 92.81(0.39) EPs-stackng [33] 73.05(1.95) 75.77(1.44) 70.80(2.10) 83.62(1.54) 84.99(0.98) 82.30(1.65) 88.17(0.41) 89.01(0.43) 87.20(0.45) EPs-F 82.72(2.22) 84.00(1.90) 81.31(2.41) 93.81(0.94) 94.09(0.79) 93.32(0.14) 96.26(0.53) 96.46(0.49) 95.96(0.58) of Pava, and Houston Unversty s 700, 500, 500 respectvely and h s set to be 500 for these three mages. F. Effect of Dfferent Number of EP groups In ths part, the nfluence of number of EP groups to proposed EPs-F method s nvestgated. Specfcally, for Indan Pnes and Pava Unversty mages, the frst fve EPs are generated wth the frst fve ICs, and each of them as well as ther combnatons are analyzed. Also, for the Houston unversty mage, the frst four EPs are generated wth the frst four ICs, and each of them as well as ther combnatons are analyzed. Fg. 13 (as shown n the below) llustrates the classfcaton results of the proposed method wth dfferent EP groups on three test mages of dfferent tranng samples. As can be observed, utlzng the EP 1 + EP 2 + EP 3 (proposed EPs-F) almost delvers the best classfcaton results. Further utlzng more EPs does not show any mprovements and even deterorate the performance. Therefore, n ths paper, the frst three ICs s utlzed. Overall, by consderng three dfferent mages wth dverse characterstcs (.e., spatal and spectral resoluton) and tranng samples, the frst three ICs can always delver the best performance. Therefore, only three ICs are expected to lead to very good results and t s not necessary to adjust ths parameter f other mages are used. V. CONCLUSIONS In ths paper, a new two step-based fuson framework s proposed to explot the spatal nformaton of the extncton profle (EP) for hyperspectral mage (HSI) classfcaton, whch s

11 11 TABLE V THE AVERAGE ACCURACY (STANDARD DEVIATION)(%) OF TEN REPEATED EXPERIMENTS ON THREE TEST IMAGES OBTAINED BY EPS-F AND ITS TWO VARIANT METHODS. Method OA AA K Method OA AA K Method OA AA K Indn Pnes EPs-F wthout EPs-F wthout Fuson Stage II Fuson Stage I EP 1 EP 2 EP 3 EPs-F (3.42) (2.93) (3.24) (5.52) (3.34) (3.98) (4.20) (2.80) (3.43) (3.98) (4.41) (5.24) (3.69) (4.67) (4.31) Unversty of Pava EPs-F wthout EPs-F wthout Fuson Stage II Fuson Stage I EP 1 EP 2 EP 3 EPs-F ) (2.72) (2.34) (2.60) (4.84) (2.56) (1.44) (1.52) (1.64) (2.32) (1.50) (3.42) (2.93) (3.24) (5.52) (3.34) Houston Unversty EPs-F wthout EPs-F wthout Fuson Stage II Fuson Stage I EP 1 EP 2 EP 3 EPs-F (2.32) (3.35) (2.66) (2.11) (2.22) (2.00) (3.03) (2.16) (1.66) (1.90) (2.52) (3.24) (2.92) (2.32) (2.42) termed as the EPs-F method. Frst, by extractng the EP from frst three ndependent components (ICs) of HSI, three dscrmnatve and complementary EPs can be constructed. Then, a superpxel-based adaptve composte kernel strategy s proposed to ncorporate the spatal nformaton wthn each EP and spectral features of the orgnal HSI mage. Furthermore, a decson fuson strategy s utlzed to fuse complementary nformaton among EPs. In ths way, the abundant spatal nformaton wthn each EP, the spectral feature of orgnal HSI and the complementary nformaton among EPs are well utlzed. The classfcaton results obtaned by the proposed EPs-F on three real HSIs outperforms several state-of-theart classfcaton methods n term of quanttve and vsual performance whch proves the effectveness of the proposed method. In the proposed framework, the superpxel-based method s used to explore the abundant nformaton wthn EP. However, snce the EP s also a 3D mage, the tensor analyss-based method deserves to be studed to extract more dscrmnatve features for classfcaton. Moreover, our future work wll utlze other knds of features and combne them wth EPs to mprove classfcaton accuraces. ACKNOWLEDGEMENT The authors would lke to thank Prof. D. Landgrebe from Purdue Unversty and the NASA Jet Propulson Laboratory for provdng the free downloads of the hyperspectral data sets. In addton, we would apprecate Dr. Jun L for provdng the Fg. 12. The effects of the parameter h n equaton (10) to the classfcaton accuracy on three test mages wth Tr = 50. Fg. 13. The effects of the weght s dstrbuton n equaton (10) to the classfcaton accuracy on three test mages wth dfferent Tr. softwares of GCK-MLR and MNFL. We would lke also to thank the edtors and anonymous revewers for ther nsghtful comments and suggestons, whch have sgnfcantly mproved ths paper. REFERENCES [1] M. Dalponte, H. O. Orka, T. Gobakken, D. Ganelle, and E. Nasset, Tree speces classfcaton n boreal forests wth hyperspectral data, IEEE Trans. Geosc. Remote Sens., vol. 51, no. 5, pp , May [2] K. Karalas, G. Tsagkataks, M. Zervaks, and P. Tsakaldes, Land classfcaton usng remotely sensed data: Gong multlabel, IEEE Trans. Geosc. Remote Sens, vol. 54, no. 6, pp , Jun [3] J. M. Boucas-Das, A. Plaza, G. Camps-Valls, P. Scheunders, N. M. Nasrabad, and J. Chanussot, Hyperspectral remote sensng data analyss and future challenges, IEEE Geosc. Remote Sens. Mag., vol. 1, no. 2, pp. 6-36, Jun [4] F. Ratle, G. Camps-Valls, and J. Weston, Semsupervsed neural networks for effcent hyperspectral mage classfcaton, IEEE Trans. Geosc. Remote Sens., vol. 48, no. 5, pp , May [5] Q. Wang, Y. Gu, and D. Tua, Dscrmnatve multple kernel learnng for hyperspectral mage classfcaton, IEEE Trans. Geosc. Remote Sens., vol. 54, no. 7, pp , Jul [6] Y. Gu, J. Chanussot, X. Ja, and J. A. Benedktsson, Multple kernel learnng for hyperspectral mage classfcaton: A revew, IEEE Trans. Geosc. Remote Sens., n Press [7] Y. Gu, T. Lu, X. Ja, J. A. Benedktsson, and J. Chanussot, Nonlnear multple kernel learnng wth mult-structure-element extended morphologcal profles for hyperspectral mage classfcaton, IEEE Trans. Geosc. Remote Sens., vol. 54, no. 6, pp , Jun [8] S. Ja, L. Shen, and Q. L, Gabor feature-based collaboratve representaton for hyperspectral magery classfcaton, IEEE Trans. Geosc. Remote Sens., vol. 53, no. 2, pp , Feb [9] J. Lu, Z. Wu, J. L, A. Plaza, and Y. Yuan, Probablstc kernel collaboratve representaton for spatal-spectral hyperspectral mage classfcaton, IEEE Trans. Geosc. Remote Sens., vol. 54, no. 4, pp , Apr

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Remote Sens., vol. 53, no. 3, pp , Mar Leyuan Fang (S 10-M 14-SM 17) receved the B.S. and Ph.D. degrees from the College of Electrcal and Informaton Engneerng, Hunan Unversty, Changsha, Chna, n 2008 and 2015, respectvely. From September 2011 to September 2012, he was a vstng Ph.D. student wth the Department of Ophthalmology, Duke Unversty, Durham, NC, USA, supported by the Chna Scholarshp Councl. Snce Jan. 2017, he has been an assocate professor wth the College of Electrcal and Informaton Engneerng, Hunan Unversty. Hs research nterests nclude sparse representaton and mult-resoluton analyss n remote sensng and medcal mage processng. He has won the Scholarshp Award for Excellent Doctoral Student granted by Chnese Mnstry of Educaton n 2011.

13 13 Nanjun He (S 17) receved the B.S. degree from Central South Unversty of Forestry and Technology, Changsha, Chna, n He s currently workng toward the Ph.D. degree n the Laboratory of Vson and Image Processng, Hunan Unversty, Changsha, Chna. From October 2017 to October 2018, he s a vstng PH.D. student wth Hyperspectral Computng Laboratory, Unversty of Extremadura, Ca ceres, Span, supported by the Chna Scholarshp Councl. Hs research nterests nclude hyperspectral magery classfcaton, hyperspectral magery restoraton, and computer vson n remote sensng mages. Shutao L (M 07-SM 15) receved the B.S., M.S., and Ph.D. degrees from Hunan Unversty, Changsha, Chna, n 1995, 1997, and 2001, respectvely. He was a Research Assocate wth the Department of Computer Scence, the Hong Kong Unversty of Scence and Technology, Hong Kong, n From 2002 to 2003, he was a Post-Doctoral Fellow wth the Royal Holloway College, Unversty of London, London, U.K., wth Prof. John ShaweTaylor. In 2005, he vsted the Department of Computer Scence, Hong Kong Unversty of Scence and Technology as a Vstng Professor. He joned the College of Electrcal and Informaton Engneerng, Hunan Unversty, n He s currently a Full Professor wth the College of Electrcal and Informaton Engneerng, Hunan Unversty. He has authored or co-authored over 160 refereed papers. Hs current research nterests nclude compressve sensng, sparse representaton, mage processng, and pattern recognton. He s now an Assocate Edtor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, and a member of the Edtoral Board of the Informaton Fuson and the Sensng and Imagng. He was a recpent of two 2nd-Grade Natonal Awards at the Scence and Technology Progress of Chna n 2004 and Pedram Ghams (S 12-M 15) receved the B.Sc. degree n cvl (survey) engneerng from the Tehran South Campus of Azad Unversty, Tehran, Iran, the M.Sc. degree (Hons.) n remote sensng from the K. N. Toos Unversty of Technology, Tehran, n 2012, and the Ph.D. degree n electrcal and computer engneerng from the Unversty of Iceland, Reykjavk, Iceland, n From 2013 to 2014, he spent seven months wth the School of Geography, Plannng and Envronmental Management, The Unversty of Queensland, Brsbane, QLD, Australa. He was a Post-Doctoral Research Fellow wth the Unversty of Iceland. He has been a Post-Doctoral Research Fellow wth the Techncal Unversty of Munch, Munch, Germany, and Hedelberg Unversty, Hedelberg, Germany, snce He has also been a Researcher wth the Remote Sensng Technology Insttute (IMF), German Aerospace Center (DLR), Wesslng, Germany, snce Hs research nterests nclude remote sensng and mage analyss, wth a specal focus on spectral and spatal technques for hyperspectral mage classfcaton, multsensor data fuson, machne learnng, and deep learnng. Dr. Ghams receved the prestgous Alexander von Humboldt Fellowshp n In the 2010C2011 academc year, he receved the Best Researcher Award for M.Sc. students n the K. N. Toos Unversty of Technology. In 2013, he presented at the IEEE Internatonal Geoscence and Remote Sensng Symposum, Melbourne, VIC, Australa, and receved the IEEE Mko Takag Prze for wnnng the conference Student Paper Competton aganst almost 70 people. In 2016, he was selected as a talented nternatonal researcher by the Irans Natonal Eltes Foundaton. In 2017, he won the Data Fuson Contest 2017 organzed by the Image Analyss and Data Fuson Techncal Commttee of the IEEE Geoscence and Remote Sensng Socety. Hs model was the most accurate among over 800 submssons. Jon Atl Benedktsson (S 84-M 90-SM 99-F 04) receved the Cand.Sc. degree n electrcal engneerng from the Unversty of Iceland, Reykjavk, Iceland, n 1984 and the M.S.E.E. and Ph.D. degrees n electrcal engneerng from Purdue Unversty, West Lafayette, IN, USA, n 1987 and 1990, respectvely. From 2009 to 2015, he was the Pro Rector of Scence and Academc Affars and a Professor of electrcal and computer engneerng wth the Unversty of Iceland. On July 1, 2015, he became the Rector of the Unversty of Iceland. He has publshed extensvely n hs felds of nterest. Hs research nterests nclude remote sensng, mage analyss, pattern recognton, bomedcal analyss of sgnals, and sgnal processng. Dr. Benedktsson s a member of the Assocaton of Chartered Engneers at Iceland (VFI), the Socetas Scnetarum Islandca, and the Tau Beta P. He s a Fellow of the Internatonal Socety for Optcs and Photoncs (SPIE). He was a recpent of the Stevan J. Krstof Award from Purdue Unversty n 1991 as an Outstandng Graduate Student n Remote Sensng. He was also a recpent of the Icelandc Research Councl s Outstandng Young Researcher Award n 1997, the IEEE Thrd Mllennum Medal n 2000, the Yearly Research Award from the Engneerng Research Insttute at the Unversty of Iceland n 2006, the Outstandng Servce Award from the IEEE Geoscence and Remote Sensng Socety n 2007, and the IEEE/VFI Electrcal Engneer of the Year Award n He was a co-recpent of the Unversty of Iceland s Technology Innovaton Award n 2004, the 2012 IEEE Transactons on Geoscence and Remote Sensng Paper Award, the IEEE GRSS Hghest Impact Paper Award n 2013, and the Internatonal Journal of Image and Data Fuson Best Paper Award n He was the Presdent of the IEEE Geoscence and Remote Sensng Socety (GRSS) and has been on the GRSS Admnstratve Commttee snce He was the Edtor-n-Chef of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) from 2003 to 2008 and has been an Assocate Edtor of TGRS snce 1999, the IEEE GEOSCIENCE AND REMOTE SENSING LETTERS snce 2003, and the IEEE ACCESS snce He was the Charman of the Steerng Commttee of the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING from 2007 to He serves on the Edtoral Board of the IEEE PROCEEDINGS and the Internatonal Edtoral Board of the Internatonal Journal of Image and Data Fuson. He s the Co-Founder of a bomedcal startup company Oxymap.