Gabor-Flterng-Based Completed Loal Bnary Patterns for Land-Use Sene Classfaton Chen Chen 1, Lbng Zhou 2,*, Janzhong Guo 1,2, We L 3, Hongjun Su 4, Fangda Guo 5 1 Department of Eletral Engneerng, Unversty of Texas at Dallas, TX, USA (E-mal: henhen870713@gmal.om) 2 Shool of Eletron and Eletral Engneerng, Wuhan Textle Unversty, Wuhan, Chna 3 College of Informaton Sene and Tehnology, Bejng Unversty of Chemal Tehnology, Bejng, Chna 4 Shool of Earth Senes and Engneerng, Hoha Unversty, Nanjng, Chna 5 Department of Eletral, Computer, and Bomedal Engneerng, Unversty of Pava, Pava, Italy Abstrat Remote sensng land-use sene lassfaton has a wde range of applatons nludng forestry, urban-growth analyss, and weather foreastng. Ths paper presents an effetve mage representaton method, Gabor-flterng-based ompleted loal bnary patterns (GCLBP), for land-use sene lassfaton. It employs the mult-orentaton Gabor flters to apture the global texture nformaton from an nput mage. Then, a loal operator alled ompleted loal bnary patterns (CLBP) s utlzed to extrat the loal texture features, suh as edges and orners, from the Gabor feature mages and the nput mage. The resultng CLBP hstogram features are onatenated to represent an nput mage. Expermental results on two datasets demonstrate that the proposed method s superor to several exstng methods for landuse sene lassfaton. Keywords-Gabor flterng; loal bnary patterns; land-use sene lassfaton; extreme learnng mahne I. INTRODUCTION Land-use sene lassfaton ams to assgn semant labels (e.g., buldng, rver, forest, mountan, et.) to aeral or satellte mages. It has a wde range of applatons nludng agrultural plannng, forestry, urban-growth analyss, and land use management. Wth the rapd development n sensor tehnology, hgh-resoluton remote sensng mages an be obtaned usng the advaned spae-borne sensors. Hgh-resoluton remote sensng mages wth rh spatal and texture nformaton have made t possble to ategorze dfferent landuse sene lasses automatally [1]. There has been a great deal of effort n employng omputer vson tehnques for lassfyng aeral or satellte mages. The Bag-of-Words (BoW) model [2] s one of the most popular approahes n mage lassfaton and mage retreval applatons. In the BoW model, loal mage features suh as olor and texture are frst quantzed nto a set of vsual words usng some lusterng methods. An mage s then represented by frequenes of the set of vsual words. Although the BoW model has demonstrated the effetveness for the remotely sensed land-use sene lassfaton [1, 3], t gnores spatal relatonshps of the loal features. To norporate spatal ontext to the BoW model, a spatal pyramd mathng (SPM) framework was proposed n [4] by parttonng an mage nto subregons and omputng a BoW hstogram for eah subregon. Hstograms from all subregons were onatenated to form the SPM representaton of an mage. In [5], a mult-resoluton representaton was norporated nto the BoW model to mprove the SPM framework by onstrutng multple resoluton mages and extratng loal features from all the resoluton mages wth dense regons. Sne the SPM method uses the absolute spatal nformaton, t may not mprove the lassfaton performane for mages exhbt rotaton and translaton varatons due to rotated amera vews. To overome ths lmtaton, a pyramd-ofspatal-relatons (PSR) model was proposed n [6] to apture both absolute and relatve spatal relatonshps of loal features. The above-mentoned methods foused on mprovng the BoW framework by norporatng spatal nformaton for land-use sene lassfaton; however, extratng effetve loal features that an apture the rh texture nformaton of the hgh-resoluton remote sensng mages was not exploted. On the other hand, some works evaluated varous mage feature desrptors and ombnatons of feature desrptors for sene lassfaton. In [7], loal strutural texture desrptors and strutural texture smlarty wth nearest neghbor lassfer were utlzed for semant lassfaton of aeral mages. In [8], Gabor desrptor and Gst desrptor were evaluated ndvdually for the task of aeral mage lassfaton. In [9], a global feature desrptor named enhaned Gabor texture desrptor (EGTD) and a loal sale-nvarant feature transform (SIFT) [10] desrptor were ombned n a herarhal approah to mprove the remote sensng mage lassfaton performane. In [11], four types of features onsst of DAISY [12], geometr blur [13], SIFT [10], and self-smlarty [14] were used wthn the framework of multfeature jont sparse odng wth spatal relaton onstrant. Although fusng a set of dfferent features may enhane the dsrmnatve power, t requres parameter tunng for eah feature and the feature dmensonalty may be nreased sgnfantly. Gabor flters [15] and loal bnary patterns (LBP) [16] have been suessfully appled for a varety of mage proessng and mahne vson applatons (e.g., [17-19]). In ths paper, we present an effent mage representaton method usng Gabor-flterng-based ompleted loal bnary patterns (GCLBP). More spefally, multorentaton Gabor flters are frst appled to a remotely sensed nput mage to obtan multple Gabor feature mages whh apture dfferent orentaton nformaton of the nput mage. Completed loal bnary patterns (CLBP) [20] operator, a omplete modelng of the LBP operator, s then employed to extrat the rotaton nvarant texture features (hstograms) from the Gabor feature mages as well as the nput mage. The overall framework of the proposed representaton approah s llustrated n Fg. 1. For lassfaton, kernel-based extreme learnng mahne (KELM) [21] s utlzed due to ts effent omputaton and good lassfaton performane. The remander of ths paper s organzed as follows. Seton II provdes relevant bakground and related work. Seton III desrbes the detals of the proposed mage representaton approah. Seton IV presents the expermental data and setup as well as omparson of the lassfaton performane between the proposed method and the exstng methods. Fnally, Seton V makes several onludng remarks. * Correspondene to Lbng Zhou (lb_zhou@163.om). Ths researh was supported by the Key Program of Hube Provnal Department of Eduaton (Grant No. D20141602).
Fg. 1. The framework of the proposed GCLBP mage representaton approah. II. RELATED WORK A. Gabor Flterng A Gabor wavelet s a flter whose mpulse response s defned by a snusodal wave multpled by a Gaussan funton. In the 2-D spatal doman, a Gabor flter, nludng a real omponent and an magnary term, an be represented as G 2 2 2 2 a b a,,,, a, b exp exp j 2 2 where (1) a aos bsn (2) b asn bos. (3) Here, a and b denote the pxel postons, represents the wavelength of the snusodal fator, represents the orentaton of the Gabor wavelet (e.g., 8, 4, 2, et.). Note that we only o o need to onsder [0,180 ] sne symmetry makes other dretons redundant. s the phase offset and s the spatal aspet rato (the default value s 0.5 [17, 18]) spefyng the ellptty of the support of the Gabor funton. 0 and 2 return the real and magnary parts of the Gabor flter, respetvely. Parameter s the standard devaton of the Gaussan funton and t s determned by and spatal frequeny bandwdth bw as bw ln 2 2 1 bw. 2 2 1 A vsualzaton of Gabor flters for four orentatons s presented n Fg. 2. (4) ( rsn(2 m), ros(2 m)). The LBP s omputed by thresholdng 1 the neghbors {} m t 0 wth the enter pxel t to generate an m -bt bnary number. The resultng LBP for t an be expressed n demal form as follows: m1 m1 2 2, (5) LBP t s t t s d m, r 0 0 where d ( t t ) s the dfferene between the enter pxel and eah neghbor, sd ( ) 1 f d 0 and sd ( ) 0 f d 0. The LBP only uses the sgn nformaton of d whle gnorng the magntude nformaton. However, the sgn and magntude are omplementary and they an be used to exatly reonstrut the dfferene d. In the CLBP sheme, the mage loal dfferenes are deomposed nto two omplementary omponents: the sgns and magntudes (absolute values of d,.e., d ). Fg. 3 shows an example of the sgn and magntude omponents of the CLBP extrated from a sample blok. Note that 0 s oded as -1 n CLBP [see Fg. 3 ()]. Two operators, namely CLBP-Sgn (CLBP_S) and CLBP-Magntude (CLBP_M), are used to ode these two omponents. CLBP_S s equvalent to the tradtonal LBP operator. The CLBP_M operator s defned as follows: m1 1, u CLBP _ M m, r p d, 2, pu, 0 0, u (6) where s a threshold that s set to the mean value of d from the whole mage. The CLBP-Center part whh odes the values of the enter pxels s not used here. Fg. 2. Two-dmensonal Gabor kernels wth four orentatons, from left to rght: 0, 4, 2, and 3 4. Typally, the Gabor texture feature mage n a spef orentaton s the magntude part of onvolvng the nput mage wth the Gabor funton G( a, b ). B. CLBP LBP [16] s a smple yet effent operator to summarze loal gray-level struture of an mage. Gven a enter pxel t, ts neghborng pxels are equally spaed on a rle of radus r ( r 0 ) wth the enter at t. If the oordnates of t are (0,0) and m 1 neghbors {} m t 0 are onsdered, the oordnates of t are Fg. 3. (a) 3 3 sample blok; (b) the loal dfferenes; () the sgn omponent of CLBP; and (d) the magntude omponent of CLBP. C. Extreme Learnng Mahne (ELM) ELM [22] s an effent learnng algorthm for sngle-hddenlayer feed-forward neural networks (SLFNs). The hdden node parameters n ELM are randomly generated leadng to a muh faster learnng rate. Let y [ y1,..., y,..., ] T C k yc be the lass to whh a sample belongs, where yk {1, 1} ( 1k C ) and C s the number of lasses. Gven n tranng samples {, } n M x y 1, where x and y C, the model of a sngle hdden layer neural network havng L hdden nodes an be expressed as
L j1 β jh w j x ej y, 1,..., n, (7) where h() s a nonlnear atvaton funton (e.g., Sgmod funton), C β j denotes the weght vetor onnetng the j th hdden node M to the output nodes, w j denotes the weght vetor onnetng the j th hdden node to the nput nodes, and e j s the bas of the j th hdden node. The above n equatons an be wrtten ompatly as: Hβ Y, (8) T T LC T T nc where β [ β1 ;...; β L], Y [ y1 ;...; y n ], and H s the hdden layer output matrx of the neural network expressed as h( x1) h( w1 x1 e1 ) h( w L x1 el) H h( x ) h( w x e ) h( w x e ) n 1 n 1 L n L nl. (9) CLBP_M). Eah Gabor feature mage results n one CLBP_S oded mage (equvalent to an LBP oded mage) and one CLBP_M oded mage. Fg. 4 (a1) - (e1) show the CLBP_S oded mages for the Gabor feature mages and Fg. 4 (a2) - (e2) show the CLBP_M oded mages for the Gabor feature mages. It s obvous that the detaled loal spatal texture features, suh as edges, orners, and knots, are enhaned n the CLBP_S and CLBP_M oded mages. Moreover, CLBP_S and CLBP_M oded mages ontan omplementary texture nformaton whh motvates us to use CLBP n our mage representaton method to enhane the dsrmnatve power. The CLBP operator s also appled to the nput mage. Hstogram s omputed from eah CLBP_S and CLBP_M oded mages. Fnally, all the hstograms are onatenated or staked as a omposte feature vetor before t s fed nto a KELM lassfer. The overall framework of the proposed mage representaton approah (GCLBP) s llustrated n Fg. 1. Note that we use rotaton nvarant pattern n CLBP to aheve mage rotaton nvarane. h( x ) [ h( w x e ),..., h( w x e )] s the output of the hdden 1 1 L L nodes n response to the nput x. A least-squares soluton ˆβ of the lnear system Hβ Y s found to be ˆ, β HY (10) where H s the Moore-Penrose generalzed nverse of matrx H. As a result, the output funton of the ELM lassfer an be expressed as T I T fl( x) h( x) β h( x) H HH Y, (11) where 1 s a regularzaton term. A kernel matrx h( x ) h( x ) K( x, x ) s onsdered f the feature mappng ELM j j hx ( ) s unknown. Therefore, the output funton of KELM s gven by 1 K( x, x1) 1 I fl( x) ELM Y. K(, n) x x T (12) The label of a test sample s assgned to the ndex of the output nodes wth the largest value. III. PROPOSED IMAGE REPRESENTATION APPROACH Inspred by the suess of Gabor flters and LBP n omputer vson applatons, we propose an effent mage representaton approah for land-use sene lassfaton usng Gabor-flterng-based CLBP. The Gabor flter belongs to a global operator whle LBP s a loal one. As a onsequene, Gabor features and LBP features represent texture nformaton from dfferent perspetves. An nput land-use sene mage s frst onvolved wth the Gabor flters wth dfferent orentatons to generate the Gabor-fltered mages. The magntudes of the Gabor-fltered mages are used as the Gabor texture feature mages. Fg. 4(b) - (e) are the Gabor feature mages obtaned by the Gabor flters wth four orentatons ( =0, = 4, = 2, and =3 4 ). As we an see the Gabor feature mages reflet the global sgnal power n dfferent orentatons. In order to enhane the nformaton n the Gabor feature mages, we enode the Gabor feature mages wth the CLBP operator (.e., CLBP_S and Fg. 4. Examples of Gabor feature mages and the orrespondng CLBP oded mages. (a) Input mage. (b) - (e) are the Gabor feature mages obtaned by the Gabor flters wth =0, = 4, = 2, and =3 4 (wavelength 8 and bandwdth bw 4 ). (a1) - (e1) are CLBP_S oded mages orrespondng to (a) - (e). (a2) - (e2) are CLBP_M oded mages orrespondng to (a) - (e). The pxel values of the CLBP_S (CLBP_M) oded mages are CLBP_S (CLBP_M) odes (bnary strngs) n demal form. IV. EXPERIMENT To evaluate the effay of our proposed mage representaton method for remote sensng land-use sene lassfaton, we ondut experments usng two publly avalable datasets. The lassfaton performane of the proposed method s ompared wth the state-ofthe-art performane reported n the lteratures. In our experments, the radal bass funton (RBF) kernel was employed n KELM. A. Expermental Data and Setup The frst dataset s the 21-lass land-use dataset wth ground truth labelng [3]. The dataset onssts of mages of 21 land-use lasses seleted from aeral orthomagery. Eah lass ontans 100 mages wth szes of 256 256 pxels. Ths s a hallengng dataset due to a varety of spatal patterns n those 21 lasses. Sample mages of eah land-use lass are shown n Fg. 5. To faltate a far omparson, the same expermental settng reported n [3] was followed. Fve-fold ross-valdaton s performed n whh the dataset s randomly parttoned nto fve equal subsets. There are 20 mages from eah land-use lass n a subset. Four subsets are used for tranng and the remanng subset s used for testng. The lassfaton auray s the average over the fve ross-valdaton evaluatons. The seond dataset used n our experments s the 19-lass satellte sene dataset [23]. It onssts of 19 lasses of hgh-resoluton
satellte senes olleted from Google Earth (Google In.). There are 50 mages wth szes of 600 600 pxels for eah lass. The mages are extrated from large satellte mages. An example of eah lass s shown n Fg. 6. The same expermental setup n [24] was used. We randomly selet 30 mages per lass as tranng data and the remanng mages as testng data. The experment s repeated 10 tmes wth dfferent realzatons of randomly seleted tranng and testng mages and lassfaton auray s averaged over the 10 trals. Fg. 5. Examples from the 21-lass land-use dataset: (1) agrultural, (2) arplane, (3) baseball damond, (4) beah, (5) buldngs, (6) haparral, (7) dense resdental, (8) forest, (9) freeway, (10) golf ourse, (11) harbor, (12) nterseton, (13) medum densty resdental, (14) moble home park, (15) overpass, (16) parkng lot, (17) rver, (18) runway, (19) sparse resdental, (20) storage tanks, (21) tenns ourts. [0, 6, 3, 2,2 3,5 6], and eght orentatons nlude [0, 8, 4,3 8, 2,5 8,3 4,7 8]. Fg. 9 llustrates the lassfaton performane of GCLBP wth dfferent orentatons. Thus, four orentatons nlude [0, 4, 2,3 4] were hosen for the experments. Then, we assgn approprate values for the parameter set ( mr, ) of the CLBP operator. The lassfaton results wth varous CLBP parameter sets are lsted n Tables I and II for the two datasets, respetvely. Note that the dmensonalty of the CLBP hstogram features s dependent on the number of neghbors ( m ). Therefore, larger m wll nrease the feature dmensonalty and omputatonal omplexty. In our experments, we hoose ( mr, ) (10,3) for the 21- lass land-use dataset and ( mr, ) (8,3) for the 19-lass satellte sene dataset n terms of lassfaton auray and omputatonal omplexty, makng the dmensonaltes of the GCLBP features for the 21-lass land-use dataset and the 19-lass satellte sene dataset 1080 and 360, respetvely. Furthermore, n all the experments, the parameters for KELM (RBF kernel parameters) were hosen as the ones that maxmzed the tranng auray by means of a 5-fold ross-valadaton. Fg. 6. Examples from the 19-lass satellte sene dataset: (1) arport, (2) beah, (3) brdge, (4) ommeral, (5) desert, (6) farmland, (7) football feld, (8) forest, (9) ndustral, (10) meadow, (11) mountan, (12) park, (13) parkng, (14) pond, (15) port, (16) ralway staton, (17) resdental, (18) rver, (19) vadut. B. Parameter Tunng Frst of all, we study the Gabor flter parameters for land-use sene lassfaton. Aordng to (4), the parameters of Gabor flter wth dfferent and bw are nvestgated. Four Gabor orentatons ( =0, = 4, = 2, and =3 4 ) are used. The parameters for the CLBP operator are set as: m =10 and r =3. For the 21-lass landuse dataset, we randomly selet four subsets for tranng and the remanng subset for testng. For the 19-lass satellte sene dataset, 30 mages per lass are randomly seleted for tranng and the remanng mages for testng. Fg. 7 and 8 show the lassfaton results for the two datasets, respetvely. From the results, the optmal for the 21-lass land-use dataset s 8 and the optmal bw s 4. The optmal for the 19-lass satellte sene dataset s 6 and the optmal bw s 2. Therefore, we fx these parameters n our subsequent experments. We further examne dfferent hoes of orentatons for the Gabor flter. Two orentatons nlude [0, 2], four orentatons nlude [0, 4, 2,3 4], sx orentatons nlude Fg. 7. Classfaton auray (%) versus varyng and bw for the proposed GCLBP method for the 21-lass land-use dataset. Fg. 8. Classfaton auray (%) versus varyng and bw for the proposed GCLBP method for the 19-lass satellte sene dataset.
the land-use sene lasses lsted n Fg. 5. The dagonal elements of the matrx denote the mean lass-spef lassfaton auray (%). TABLE III. COMPARISON OF CLASSIFICATION ACCURACY (MEAN STD) ON THE 21-CLASS LAND-USE SCENE DATASET Fg. 9. Classfaton auray (%) versus dfferent Gabor flter orentatons for the proposed GCLBP. Method Auray (%) BoW [3] 76.8 SPM [3] 75.3 BoW+Spatal Co-ourrene Kernel [3] 77.7 Color Gabor [3] 80.5 Color hstogram (HLS) [3] 81.2 Strutural texture smlarty [7] 86.0 Wavelet BoW [25] 87.4 Conentr rle-strutured multsale BoW [27] 86.6 Multple feature fuson [26] 89.5 Pyramd-of-Spatal-Relatons (PSR) [6] 89.1 CLBP 85.5 Ours (GCLBP) 90.0± 2.1 TABLE I. CLASSIFICATION ACCURACY (%) OF GCLBP WITH DIFFERENT PARAMETERS ( mr, ) OF THE CLBP OPERATOR ON THE 21-CLASS LAND-USE DATASET 21-lass land-use dataset r 1 2 3 4 5 m 4 85.48 85.00 83.57 82.86 80.71 m 6 88.81 88.10 87.14 86.67 85.71 m 8 89.52 89.05 89.76 88.33 87.38 m 10 89.05 89.29 90.24 88.10 86.67 m 12 89.52 89.52 90.48 90.00 88.10 TABLE II. CLASSIFICATION ACCURACY (%) OF GCLBP WITH DIFFERENT PARAMETERS ( mr, ) OF THE CLBP OPERATOR ON THE 19-CLASS SATELLETE SCENE DATASET 19-lass satellte sene dataset r 1 2 3 4 5 m 4 85.79 89.21 87.89 87.11 85.00 m 6 86.84 89.47 88.68 89.21 88.68 m 8 89.74 90.53 91.84 90.79 90.53 m 10 89.21 90.26 91.32 91.58 91.05 m 12 89.74 91.32 91.32 91.32 91.05 C. Comparson Wth the State of the Art To evalute the effetveness of the proposed GCLBP representaton method, a omparson of ts performane wth prevsouly reported performane n the lteratures was arred out on the 21-lass land-use dataset under the same expermental setup (.e., 80% of the mages from eah lass are used as tranng, and the remanng mages are used as testng). Sne the mages n the dataset are olor mages, we onvert the mages from the RGB olor spae to the YCbCr olor spae and use the Y omponent (lumnane) to obtan the gray sale mages. The GCLBP features are extrated from the gray sale mages. We also mplement the method whh uses the CLBP operator on the nput mage only, denoted as CLBP. The omparson results are reported n Table III, whh demonstrates that our method aheves superor lassfaton performane over the other methods. Espeally, our method aheved better performane than the popular BoW lassfaton framework, whh demonstrates the effetveness of the proposed GCLBP approah for remote sensng land-use sene lassfaton. Moreover, the proposed GCLBP has 4.5% mprovement over the CLBP method sne the mult-orentaton Gabor flters aptured the global texture nformaton n dfferent dretons. We also present the onfuson matrx of our method for the 21- lass land-use dataset n Fg. 10. For a ompat representaton, numbers along the x-axs and y-axs n ths fgure are used to ndate Fg. 10. Confuson matrx of our method for the 21-lass land-use dataset. The omparson results for the 19-lass satellte sene dataset are lsted n Table IV. Although the multple features fuson method desrbed n [24] aheved hgher lassfaton auray than our method, three dfferent sets of features nludng SIFT features, Loal Ternary Pattern Hstogram Fourer (LTP-HF) features, and olor hstogram features were used, thus leadng to nreased omputatonal omplexty. The onfuson matrx of our method for the 19-lass satellte sene dataset s shown n Fg. 11. The numbers along the x- axs and y-axs n ths fgure are used to ndate the land-use sene lasses lsted n Fg. 6. TABLE IV. COMPARISON OF CLASSIFICATION ACCURACY (MEAN STD) ON THE 19-CLASS SATELLETE SCENE DATASET Method Auray Bag of olors [26] 70.6 Tree of -shapes [26] 80.4 Bag of SIFT [26] 85.5 Multfeature onatenaton [26] 90.8 Loal Ternary Pattern Hstogram Fourer (LTP-HF) [24] 77.6 SIFT+LTP-HF+Color hstogram [24] 93.6 CLBP 86.7 Ours (GCLBP) 91.0 ± 1.5
Fg. 11. Confuson matrx of our method for the 19-lass satellte sene dataset. The dmensonalty of the GCLBP features an be farly hgh, e.g., t s 1080 for the 21-lass land-use dataset, f a large m s used for the CLBP operator. To gan omputatonal effeny, dmensonalty reduton tehnques suh as prnpal omponent analyss (PCA) [28] an be appled to the GCLBP features to redue the dmensonalty. V. CONCLUSION In ths paper, an effetve mage representaton method for remote sensng land-use sene lassfaton was ntrodued. Ths representaton method was derved from the Gabor flters and the ompleted loal bnary patterns (CLBP) operator. Gabor flters were employed to apture the global texture nformaton from dfferent dretons of an nput mage, whereas CLBP hstogram features were extrated from the Gabor feature mages to enhane the texture nformaton (e.g., edges and orners). The ombnaton of a global operator (Gabor flters) and a loal operator (CLBP) greatly enhaned the representaton power of the spatal hstogram. 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