Hyperspectral Image Classification Based on Local Binary Patterns and PCANet

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1 Hyperspectral Image Classfcaton Based on Local Bnary Patterns and PCANet Huzhen Yang a, Feng Gao a, Junyu Dong a, Yang Yang b a Ocean Unversty of Chna, Department of Computer Scence and Technology b Ocean Unversty of Chna, Law & Poltcs School ABSTRACT Hyperspectral mage classfcaton has been well acknowledged as one of the challengng tasks of hyperspectral data processng. In ths paper, we propose a novel hyperspectral mage classfcaton framework based on local bnary pattern (LBP) features and PCANet. In the proposed method, lnear predcton error (LPE) s frst employed to select a subset of nformatve bands, and LBP s utlzed to extract texture features. Then, spectral and texture features are stacked nto a hgh dmensonal vectors. Next, the extracted features of a specfed poston are transformed to a -D mage. The obtaned mages of all pxels are fed nto PCANet for classfcaton. Expermental results on real hyperspectral dataset demonstrate the effectveness of the proposed method. Keywords: local bnary patterns, hyperspectral mage, pattern classfcaton, prncpal component analyss, convolutonal network.. INTRODUCTION Wth the development of remote sensng technology, hyperspectral mages wth hgh spatal resoluton are easy to obtan n our daly lfe. Over the past two decades, hyperspectral mages have been wdely appled n the feld of urban land cover montorng [], vegetaton classfcaton [], envronmental montorng [3], agrcultural exploraton [4], etc. Hyperspectral sensor provdes hundreds of narrow spectral channels from the same area on the surface of the earth. The detaled spectral nformaton ncreases the capablty of dstngushng between dfferent land-cover classes wth promotng accuracy. In ths paper, we manly focus on the problem of land-cover classfcaton by usng hyperspectral mages. In the early studes of hyperspectral mage classfcaton, researchers only used the spectral nformaton [5-7]. Camps-Valls et al. [5] proposed a kernel-based method for hyperspectral mage classfcaton. In the method, regularzed radal bass functon neural networks are ntroduced to mprove the classfcaton performance. Demr and Erturk [8] proposed a hyperspectral mage classfcaton method based on relevance vector machnes. The method s of hgh computatonal effcency and more sutable for real-tme classfcaton applcatons. Later, some researchers recognzed that the combnaton of spectral and spatal nformaton could provde better performance. Therefore, many classfcaton methods based on spectral-spectral features are proposed. In [9], a method based on extended morphologcal profle was proposed for fuson of the spatal and spectral nformaton. L et al. [0] used Gabor feature for hyperspectral mage spatal nformaton analyss. Recently, deep learnng methods have dsplayed promsng performance for hyperspectral mage classfcaton. In [], deep belef network was appled n hyperspectral mage classfcaton. Chen et al. [] employed a stacked autoencoder to extract features of hyperspectral mage. In [3], convolutonal autoencoder was utlzed to learn representatve features from hyperspectral mage. However, as mentoned n [4], deep learnng-based methods mght perform poor when the number of tranng samples s small. Chan et al. [5] proposed a smplfed deep learnng method called PCANet. It s only comprsed of two convolutonal layers and one feature generaton layer. Although the archtecture of PCANet s rather smple, t can acheve compettve results aganst many state-of-the-art deep learnng models n many classfcaton tasks. In ths paper, we propose a novel hyperspectral mage classfcaton framework based on LBP features and PCANet. In the proposed method, lnear predcton error (LPE) [6] s frst employed to select a subset of nformatve spectral bands, and LBP s utlzed to extract texture features. Then, spectral and texture features are stacked nto a hgh dmensonal vectors. Next, the extracted features of a specfed poston are transformed to a -D mage. The obtaned mages

2 of all pxels are fed nto PCANet for classfcaton. Expermental results on real hyperspectral dataset demonstrate the effectveness of the proposed method. The remander of ths paper s organzed as follows. In Secton, we present the classfcaton framework of the proposed method. Secton 3 shows the expermental results and dscussons. Fnally, Secton 4 draws concluson of the proposed method. Some hnts for plausble future research are also provded.. METHODOLOGY Fgure The framework of the proposed method. In ths secton, the detaled mplementaton of the proposed method s descrbed. The overall framework of the proposed method s llustrated n Fg.. To be specfc, the proposed hyperspectral classfcaton method s comprsed of two man steps: Step Extracton of the spectral and texture features. In the spectral feature extracton, we use all the spectral channels as nput. In the texture feature extracton, LPE [6] s utlzed to select a subset of nformatve spectral bands, and LBP [7] s employed to extract texture features. Then, spectral and texture features are stacked nto hgh dmensonal vectors. Step Integratng the texture and spectral features nto PCANet for classfcaton. The extracted spectral and texture features for each pxel are transformed from a -D vector to a -D mage. Then, the obtaned mages are fed nto PCANet for classfcaton.. Extracton of the texture and spectral features Feature extracton n the proposed method s conssted of two parallel modules: spectral feature extracton and texture feature extracton. In the spectral feature extracton, all the spectral channels are used as nput, as shown n Fg.. Fgure Implementaton of LBP feature extracton. In texture feature extracton, f all the spectral channels are used n the texture feature extracton, the complexty of texture feature extracton wll ncrease the computatonal burden. Therefore, we select nformatve bands to reduce the computatonal complexty before extractng texture feature. In ths paper, LPE [6] s utlzed to select the most dstnc-

3 tve and nformatve bands. LPE s a smple but effectve band selecton method, and based on band smlarty measurement. Specfcally, the basc steps of LPE can be summarzed as follows: Frst, choose a par of bands B and B, and the resultng selected band subset s F = { B, B}. Second, fnd a thrd band B 3 that s the most smlar to all the bands n the current F. Then, the selected band subset wll be updated as F = F? { B3}. Fnally, contnue the second step untl we obtan enough bands. In LPE, the employed smlarty crtera s lnear predcton. In our mplementatons, we select 7 bands for texture feature extracton. After band selecton, LBP operator whch descrbes the edges, lnes, and flat areas of the texture nformaton, s utlzed to each selected band. Gven a center pxel t c, each neghbor pxels of a local regon s assgned wth a bnary label, whch s ether or 0. To be specfc, the k neghborng pxels are generated from a crcle of radus r centered at t c. The LBP code for the center pxel t c can defned as: k - LBP ( t ) = å U( t - t ), () k, r c c = where U( t - tc) = 0 f t t, and U( t ) c - tc = f t > t. The LBP code presents the texture nformaton n a c local regon. After obtanng the LBP codes of all the pxels, a hstogram wll be computed over a local patch centered at the nterested pxel, as llustrated n Fg.. All bands of LBP hstograms are concatenated to form the texture feature vector.. Integratng the texture and spectral features nto PCANet for classfcaton The spectral features contan crucal nformaton for dscrmnatng dfferent knds of ground categores. The texture features decrease the ntra-class varance, and then can mprove the classfcaton performance. The ntegraton of spectral and texture features provdes sgnfcant performance mprovements, t s addressed by vector stackng, as shown n Fg.. Next, the stacked features are magng to an mage. Here magng refers to transform from a -D vector to a -D mage. Supposng that v s the stacked feature vector of a pxel, and then the magng process s denoted by: v V Î k k, () In ths paper, the wdth and heght of the magng results are set the same for smplfyng. Next, the magng results are fed nto PCANet for classfcaton. Fgure 3 Flowchart of PCANet. PCANet s a smplfed deep learnng framework, whch s comprsed of PCA and bnary hashng. Its process s dvded nto three layers: the frst and second are PCA convolutonal layers, the thrd s hashng and classfcaton layer, as shown n Fg. 3.

4 In the frst layer, supposng that V s an nput sample mage. PCANet frst takes a k k patch around each pxel, and collects all the vectorzed patches to form a matrx X Î k k n. Here n s the number of patches extracted from V. Next, we construct a matrx for each nput mage and combne them together: X = [ X, X, K, X ]?? k k Nn N, () where N s the number of nput mages. PCA s utlzed to mnmze the reconstructon error wthn a famly of orthonormal flters. The flters are expressed as: W B mat ( q ( XX T )) Î? k k, l =,, K, L, () l kk l where mat kk ( ) s a functon that reshapes a vector to a matrx, and q ( XX T ) l represents the prncple vector of XX T. L corresponds to the number of flters n the frst layer. Therefore, the output of the frst layer can be obtaned by l V B V * Wl, =,, K, N. () Then we conduct the same process as that of the frst layer for all the V l, the output of the second layer s obtaned. Assumng that the number of flters n the second layer s L, we would obtan LL mages. In the fnal layer of PCANet, a bnary quantzaton process s conducted. Specfcally, each of the L mages s separated nto many local blocks. The hstogram of each block s computed, and all the hstograms are concatenated nto one vector. Moreover, SVM classfer s utlzed to determne the classfcaton results. 3. Dataset descrpton and parameter tunng 3. EXPERIMENTAL RESULTS AND ANALYSIS In ths paper, we test the proposed method and several closely related methods on the Indan Pnes dataset. The dataset s acqured n June 99 by Natonal Aeronautcs and Space Admnstraton s Arborne Vsble/Infrared Imagng Spectrometer (AVIRIS) sensor at the northwest Indana s Indan Pne test ste. The mage scene, wth a vegetatonclassfcaton scenaro of pxels and 00 spectral bands, contans two-thrds agrculture, and one-thrd forest or other natural perennal vegetaton. In our paper, the ground-truth s desgnated nto 6 classes and the number of total labeled pxels s 049. The number of tranng and testng samples s lsted n Table, respectvely. Table Class label and tran-test dstrbuton of samples n the Indan Pnes dataset. # class Tranng Testng Alfalfa Corn-notll Corn-mntll Corn Grass-pasture Grass-trees Grass-pasture-mowed Hay-wndrowed Oats Soybean-notll Soybean-mntll Soybean-clean Wheat Woods Buld-Grass-Trees-Drves Stone-Steel-Towers Total In the proposed classfcaton framework, many parameters, such as the quantty of selected bands, (, ) mr of the LBP operator and the number and sze of flters, the block sze of PCANet, are of great sgnfcance to the performance of hyperspectral classfcaton. Frstly, LPE s appled to reduce the number of channels before extractng LBP feature. In [8], the number of selectng bands and patch sze were fxed to be 7 and 7 7 and t would acheve the best perfor-

5 mance. Settng parameters n our experment s same as that. Furthermore, the parameters flter sze, flter number and patch sze of PCANet are fxed to be (5, 5), (3, 5) and (9, 9). The mpacts from dfferent ( mr, ) are nvestgated as shown n Table. 3. Expermental results Table Classfcaton accuracy by varng ( mr, ) of LBP. Value of r Value of m Classfcaton accuracy In ths subsecton, n order to demonstrate the effectveness of the proposed method, we choose sx closely related methods for comparson. These methods nclude SVM [8], ELM [9], PCANet [5], LBP_SVM, LBP_ELM [0]. For SVM, t s a very popular classfer and wdely used n hyperspectral mage classfcaton. ELM classfer s consdered to be more computatonal effcent than SVM, and has been recently appled to hyperspectral mage classfcaton. LBP_SVM and LBP_ELM are proposed by L [0]. It s demonstrated that, LBP feature provdes good performance. The overall accuracy (OA) and average accuracy (AA) are used to evaluate the classfcaton performance of dfferent methods. Table 3 Overall classfcaton accuracy (%) and average accuracy (%) by dfferent methods on the Indan Pnes dataset. Class SVM ELM PCANet LBP_SVM LBP_ELM Proposed method OA AA In the experment, we randomly choose the tranng and testng samples. The performance of each algorthm s compared by OA and AA. The classfcaton accuracy for each class s shown n Table 3. We observe that the classfcaton accuracy of the proposed method for each class s nearly better than the other methods, except for the Corn, Grasspasture and Grass-pasture-mowed. Especally, the classfcaton results of the proposed method for Hay-wndrowed, Oats, Wheat and Woods run up to %. The proposed method obtans the best OA and AA values. The quanttatve results clearly demonstrate that the LBP s a hghly dscrmnatve texture feature for hyperspectral mage analyss. In addton, the smple framework of PCANet has some advantage n hyperspectral mage classfcaton.

6 (a) (b) (c) (d) (e) (f) (g) (h) Alfalfa Corn-notll Corn-mntll Corn Grass-pasture Grass-trees Grass-pasture-mowed Hay-wndrowed Oats Soybean-notll Soybean-mntll Soybean-clean Wheat Woods Buld-Grass-Trees-Drves Stone-Steel-Towers Fgure 4 The classfcaton results usng 367 trannng samples for the Indan Pnes datase wth 6 classes. (a) Ground truth; (b) tranng set; (c) result by SVM; (d) result by ELM; (e) result by PCANet; (f) result by LBP_SVM; (g) result by LBP_ELM; (h) result by the proposed method. The maps on labeled pxels obtaned from the dfferent methods are shown n Fg. 4. These classfcaton results are consstent wth the results shown n Tables 3. The ground truth map and tranng sets are shown n Fg. 4 (a) and (b), respectvely. Results obtaned by ntegratng texture and spectral features are less nosy than the results obtaned by only spectral features. For example, the classfcaton results of LBP_SVM and LBP_ELM are more accurate than the results of SVM and ELM. In addton, Fg. 4 (e) shows that the result of PCANet s smoother than that of SVM and ELM. In Fg. 4 (h), the combnaton of LBP feature and deep feature obtans a much smoother classfcaton result than other methods that only use texture or spectral nformaton.

7 4. CONCLUSION In ths paper, we propose a novel framework for hyperspectral mage classfcaton based on LBP and PCANet. Above all, nformatve spectral bands are selected by usng LPE. In our experment, LBP and PCANet are appled to extract spatal features and deep features, respectvely. LBP operator s calculated on the local grd of the mage, and mantans a good nvarance to the shape and the llumnaton of the mage. PCANet s a smple deep learnng network, and conssts of cascaded PCA, bnary hashng, and block hstograms. To mprove the classfcaton performance, we combne hand-crafted feature and deep nformaton. Deep features (PCANet) provdes as a reasonable soluton to make up for the defect of hand-crafted features. The expermental results have demonstrated on the Indan Pnes dataset that the proposed method yelds good performance, and t gves rse to a better classfcaton result than several closely related methods. ACKNOELEDGEMENT The authors would lke to thank all the anonymous revewers for ther helpful suggestons. Ths work was supported by the Natonal Natural Scence Foundaton of Chna (No , , 67405, 65760, 64043) and n part by the Shandong Provnce Natural Scence Foundaton of Chna under Grant ZR06FB0. REFERENCES [] X. Tong, H. Xe, Q. Weng, Urban land cover classfcaton wth arborne hyperspectral data: What features to use? IEEE J. Sel. Topcs Appl. Earth Observ. Remote Sens., 7, , (04). [] M. Wang et al., The analyss about factors nfluencng the supervsed classfcaton accuracy for vegetaton hyperspectral remote sensng magery, n Internatonal Congress on Image and Sgnal Processng, pp (0). [3] H. Kallur et al., Decson-level fuson of spectral reflactance and dervatve nformaton for robust hyperspectral land cover classfcaton, IEEE Trans. Geosc. Remote Sens., 48, , (00). [4] B. Datt et al., Preprocessng EO- hyperon hyperspectral data to support the applcaton of agrcultural ndexes, IEEE Trans. Geosc. Remote Sens., 4, 46 59, (003). [5] G. Camps-Valls, L. Bruzzone, Kernel-based methods for hyperspectral mage classfcaton, IEEE Trans. Geosc. Remote Sens., 43, 35 36, (005). [6] G. Camps-Valls et al., Composte kernels for hyperspectral mage classfcaton, IEEE Geosc. Remote Sens. Lett., 3, 93 97, (006). [7] G. Camps-Valls et al., Sem-supervsed graph-based hyperspectral mage classfcaton, IEEE Trans. Geosc. Remote Sens., 45, 44-54, (007). [8] B. Demr, S. Erturk, Hyperspectral mage classfcaton usng relevance vector machnes, IEEE Geosc. Remote Sens. Lett., 4, , (007). [9] M. Fauvel et al., Spectral and spatal classfcaton of hyperspectral data usng SVMs and morphologcal profles, IEEE Trans. Geosc. Remote Sens., 46, , (008). [0] W. L, Q. Du, Gabor-flterng-based nearest regularzed subspace for hyperspectral mage classfcaton, IEEE J. Sel. Topcs Appl. Earth Observ. Remote Sens., 7, 0 0, (04). [] T. L, J. Zhang, Y. Zhang, Classfcaton of hyperspectral mage based on deep belef networks, n IEEE Internatonal Conference on Image Processng, pp , (05). [] Y. Chen et al., Deep learnng-based classfcaton of hyperspectral data, IEEE J. Sel. Topcs Appl. Earth Observ. Remote Sens., 7, , (04).

8 [3] W. Zhao et al., On combnng multscale deep learnng features for the classfcaton of hyperspectral remote sensng magery, Internatonal Journal of Remote Sensng, 36, , (05). [4] B. Pan, Z. Sh, X. Xu, R-VCANet: A new deep-learnng-based hyperspectral mage classfcaton method, IEEE J. Sel. Topcs Appl. Earth Observ. Remote Sens., 0, , (07). [5] T. H. Chan et al., PCANet: A smple deep learnng baselne for mage classfcaton? IEEE Trans. Image Process., 4, , (05). [6] Q. Du, H. Yang, Smlarty-based unsupervsed band selecton for hyperspectral mage analyss, IEEE Geosc. Remote Sens. Lett., 5, , (008). [7] T. Ojala et al., Multresoluton gray-scale and rotaton nvarant texture classfcaton wth local bnary pattern, IEEE Trans. Pattern Analyss Mach. Intell., 4, , (00). [8] F. Melgan, L. Bruzzone, Classfcaton of hyperspectral remote sensng mages wth support vector machnes, IEEE Trans. Geosc. Remote Sens., 4, , (004). [9] G. B. Huang, An nsght nto extreme learnng machnes: random neurons, random forests and kernels, Cogntve Computaton, 6, , (04). [0] W. L, C. Chen, H. Su, Q. Du, Local bnary patterns and extreme learnng machnes for hyperspectral magery classfcaton, 53, , (05).