SINCE the successful launch of high-resolution sensors and

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1 1458 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 5, MAY 2007 Classifiation of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features Yindi Zhao, Liangpei Zhang, Member, IEEE, Pingxiang Li, Member, IEEE, and Bo Huang Abstrat Gaussian Markov random fields (GMRFs) are used to analyze textures. GMRFs measure the interdependene of neighboring pixels within a texture to produe features. In this paper, neighboring pixels are taken into aount in a priority sequene aording to their distane from the enter pixel, and a step-by-step least squares method is proposed to extrat a novel set of GMRF texture features, named as PS-GMRF. A omplete proedure is first designed to lassify texture samples of QuikBird imagery. After texture feature extration, a subset of PS-GMRF features is obtained by the sequential floating forward-seletion method. Then, the maximum a posterior iterated onditional mode lassifiation algorithm is used, involving the seleted PS-GMRF texture features in ombination with spetral features. The experimental results show that the performane of lassifying texture samples on high spatial resolution QuikBird satellite imagery is improved when texture features and spetral features are used jointly, and PS-GMRF features have a higher disrimination power ompared to the lassial GMRF features, making a notable improvement in lassifiation auray from 71.84% to 94.01%. On the other hand, it is found that one of the PS-GMRF texture features the lowest order variane is effetive for residential-area detetion. Some results for IKONOS and SPOT-5 images show that the integration of the lowest order variane with spetral features improves the lassifiation auray ompared to lassifiation with purely spetral features. Index Terms Classifying texture samples, Gaussian Markov random fields (GMRFs), least squares (LS) method, priority sequene, residential-area detetion. I. INTRODUCTION SINCE the suessful launh of high-resolution sensors and the wide appliation of high spatial resolution satellite data, it has been possible to monitor environmental hanges on a small spatial sale. However, improved spatial resolution does not always lead to better lassifiation results when employing onventional spetral lassifiation methods [1]. While inreases in spatial resolution reate inreased amounts of Manusript reeived August 26, 2006; revised Deember 14, This work was supported in part by the 973 Program of the People s Republi of China under Grant 2006CB701302, in part by the National Natural Siene Foundation of China under Grants and , and in part by the Foundation of State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing under Grant Y. Zhao, L. Zhang, and P. Li are with the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan , China ( zlp62@publi.wh.hb.n). B. Huang is with the Department of Geography and Resoure Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. Digital Objet Identifier /TGRS information detail, this also reates higher spetral variane within eah lass orresponding to land-over units, whih dereases their spetral separability and results in lower lassifiation auray. Texture is a very useful property of spatial-struture information in high-resolution images [2]. By inorporating texture information in the lassifiation, heterogeneous objets and different objets with the same spetral harateristis an be deteted effetively, and higher lassifiation auraies an be obtained. Texture analysis, whih provides a omplementary tool to the interpretation of high-resolution satellite images, has reeived great attention in image proessing. Over the last two deades, many approahes to texture feature extration have been developed, ategorized roughly as feature-based methods suh as the gray-level oourrene matrix, model-based methods suh as Markov random-field (MRF) models, and strutural methods [3]. Comparison and fusion of different types of texture features an be found in the literature [4], [5]. This paper fouses on extrating texture features based on MRF models. These models haraterize spatial interations between the entral pixel of a texture image and its neighboring pixels to desribe the loal struture of an image [6]. Early work on texture lassifiation using MRF was done by Chellappa and Chatterjee [7], wherein texture features were extrated based on the Gaussian MRF (GMRF), and a simple minimum-distane lassifier was used for the texture image lassifiation. More systemati theories about satellite image analysis were built up using MRF [8], [9] with emphasis on the appliation of MRF for spatial information extration from satellite images. Torres- Torriti and Jouan [10] evaluated the performane of MRF models and Gabor filters for omputing texture properties, whih were then used as disrimination features to lassify different land-over types in syntheti aperture radar (SAR) imagery. Clausi and Deng [11] demonstrated that both texture and tonal features are neessary to properly perform segmentation and lassifiation of SAR sea-ie imagery. Subsequently, Clausi and Yue ompared MRF and gray-level o-ourrene probability (GLCP) for texture analysis of SAR sea-ie imagery [12]. However, the most relevant researh about MRF regards all the neighboring pixels equally and puts the whole neighbor pixels together into the orresponding MRF model equation [7] [12]. In this paper, the neighboring pixels will be treated with a priority sequene, in aordane with the distane from the enter pixel, and a new set of MRF texture features will be omputed based on this onsideration, followed by feature /$ IEEE

2 ZHAO et al.: CLASSIFICATION OF HIGH SPATIAL RESOLUTION IMAGERY 1459 Let S = {s =(i, j) 0 i M 1, 0 j N 1} denote the set of grid points in the M N lattie orresponding to the pixels in the image region. Supposing that the image is modeled as a fourth-order GMRF model f(s), the gray-level intensity of a pixel s has a loal onditional probability density funtion Fig. 1. First- to fifth-order neighborhood of site s. seletion to redue data dimensions and inrease omputational effiieny. The objetives of this paper are twofold: to assess the potential of new MRF texture features applied to lassifiation of high spatial resolution imagery and to assess the effet of integrating spetral features with texture features in highresolution-imagery lassifiation. The rest of this paper is organized as follows. Setion II desribes the novel MRF texture features. Setion III briefly illustrates the lassifiation method. Setion IV provides some omparison experiments using QuikBird samples. Deteting residential areas using the new MRF features is studied in Setion V, and onlusions are given in Setion VI. II. TEXTURE FEATURE EXTRACTION A texture feature extration method is a omputational method that analyzes the pixels ontained in a ertain window of an input image and generates a set of values that represents the ontents of that window. An ideal texture feature extration method should be apable of generating different values for different textures. One important property of an MRF model is that it uses a finite number of parameters to haraterize spatial interations of pixels to desribe an image region. A typial MRF model is the GMRF model, whih is widely used to model image textures by utilizing spetral as well as spatial information. A. Novel Texture Features Based on GMRF Models In order to make this paper self-suffiient, some important onepts for GMRF models are restated here. Markovianity refers to the fat that the entral pixel of an image interats with only the neighboring pixels, independent of other pixels [6]. A GMRF model speifies spatial dependenies between the entral pixel and its neighboring pixels by defining a loal onditional probability distribution, whih is assumed to be Gaussian [13]. The number and position of neighboring pixels are determined by the neighbor order defined using a Eulidean distane [14]. Up to fifth-order neighbors are depited in Fig. 1; wherein the number indiates the neighbor order to whih the underlying site belongs. Note that one neighbor set with order n inludes all the neighbors of sets with order one to n. For example, a fourth-order GMRF model entered on an image ell s, would inlude those ells marked one to four, and its orresponding neighbor set an be denoted as a set of shift vetors RN = {(0, 1), (1, 0), (1, 1), (1, 1), (0, 2), (2, 0),(1, 2), (1, 2), (2, 1), (2, 1)} {(0, 1), ( 1, 0),( 1, 1), ( 1, 1), (0, 2), ( 2, 0), ( 1, 2), ( 1, 2), ( 2, 1), ( 2, 1)}. p (f(s) f RN (s)) [ = 1 exp 2πν 1 f(s) µ ] 2 β(r)(f(s + r) µ) 2ν r RN where f RN (s) ={f(s + r) r RN} stands for the set of values at the neighboring site of the entral pixel s, µ is the mean gray value of the whole image, β(r)s are the model parameters, and ν is the onditional variane. On the other hand, the gray value of the entral pixel s, f(s) is also represented as a linear ombination of values of its neighbor pixels and an additive noise [6], obeying the following differene equation: f(s) µ = β(r)(f(s + r) µ)+e(s) (2) r RN where {e(s)} is a zero-mean Gaussian noise sequene with the orrelation struture ν, r =(0, 0) Cov [e(s),e(s + r)] = β(r)ν, r RN (3) 0, otherwise. Sine the power spetrum assoiated with (2) must be real and positive [15], there are r RN r RN and β(r) = β( r). Moreover, (2) an be rewritten by f(s) µ = e(s)+ β(r)[(f(s + r) µ)+(f(s r) µ)] r RN where RN is an asymmetri neighbor set suh that if r RN, then r RN and RN = {r r RN} {r r RN}. The parameters β(r)s and onditional variane ν desribe the GMRF models and haraterize textures. When they are unknown, they should be alulated. There are many existing methods for estimating those unknowns but none an guarantee onsisteny 1 as well as stability. 2 The hoie of the least squares (LS) method [7] is motivated by the simpliity stability tradeoff. Denote the set of the GMRF parameters by β = ol[β(r) r RN]. The LS estimate ˆβ of the model parameter vetor β is given by [ ] 1 [ ] ˆβ = Q(s)Q T (s) Q(s)(f(s) µ) 1 The estimates onverge toward the true values of the parameters. 2 The ovariane matrix in the expression for the joint probability density of MRF must be positive definite. (1) (4) (5)

3 1460 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 5, MAY 2007 where Q(s) = ol[(f(s + r) µ)+(f(s r) µ) r RN]. The estimate ˆν of the onditional variane ν is alulated by ˆν = 1 M N [ (f(s) µ) Q T (s)ˆβ] 2. (6) A general GMRF-based approah employs η = {ˆβ, ˆν} as a texture feature vetor. The lassial GMRF (CGMRF)-based method thinks that all of the neighboring pixels, whih are treated equally, interat on the enter pixel simultaneously. However, it is reasonable that neighboring pixels have influene on the enter pixel in a priority sequene, i. e., the loser the neighboring pixel to the enter pixel, the higher the priority. To study this situation, a step-by-step LS (SSLS) method is proposed, to derive a different set of GMRF texture features. For this purpose, the parameters of the GMRF model an be divided into a ertain number of groups and then estimated step-by-step, adopting the following rules. Rule 1) The number of groups is equal to the order of the GMRF model. Rule 2) The parameters for neighbors with different distanes from the enter pixel should be divided into different groups. Rule 3) The parameters for neighbors loser to the enter pixel should be estimated before those for neighbors further from the enter pixel. Note that the purpose is not to generate fasimiles of the texture but to assess a disrimination measure among different textures. For a fourth-order GMRF model, β is divided into four groups: β (1), β (2), β (3), and β (4). Eah group β (k) orresponds to a group of neighbors, denoted by RN (k) ; that is, RN (1) = {(0, 1), (1, 0)}, RN (2) = {(1, 1), (1, 1)}, RN (3) = {(0, 2), (2, 0)}, and RN (4) = {(1, 2), (1, 2), (2, 1), (2, 1)}. Let k stand for the urrent group of the model parameters, Q (k) (s) = ol[(f(s + r) µ)+(f(s r) µ) r RN (k) ], and m is the total number of groups. The SSLS estimate method is designed as follows, and the orresponding estimate parameters are denoted using the subsript unlike those from the LS method. 1) For k =1: β (1) = ν (1) = [ ] ( ) 1 T Q (1) (s) Q (1) (s) [ ] Q (1) (s)(f(s) µ) 1 M N [ ( ) ] T 2 (f(s) µ) Q (1) (1) (s) β. (7) (8) 2) For 1 <k m: [ ] ( ) 1 β (k) T = Q (k) (s) Q (k) (s) where [ ] Q (k) (s)(f(s) µ + R) (9) [ ] ν (k) = 1 k ( ) 2 T (f(s) µ) Q (i) (i) (s) β M N i=1 i=1 k 1 [ 1 ( ) T (i)] R = m (f(s) µ) Q (i) (s) β. (10) From (7) (10), it is obvious that the parameters in the lowlevel group are omputed independent of those in the higher level groups; however, the parameters in the high-level groups are estimated based on those in the lower level groups. For example, the vetor of the parameters in the first group β (1) is alulated by (7), not relating to the parameters or neighbors in other groups. However, the vetor of parameters in the seond group β (4) is alulated by (9) with k =4, dependent on β (1), β (2), and β (3), via the supplement variable R. For the sake of simpliity, let us denote the original GMRF features as lassial GMRF features, and denote the new GMRF features that onsider the priority sequene, omputed using the SSLS estimate method, as PS-GMRF features. A PS-GMRF feature vetor was onstruted using the results of eah estimating step, namely, ψ = {β (k), ν (k) 1 k m}.by means of SSLS, the lower order parameters are independent of those of higher orders. As a result, it is not neessary to determine the exat order of the GMRF model for a ertain texture, 3 and a higher order model is preferred. For some texture, if the order of the model used is higher than the exat order, the orrelations between the enter pixel and the higher order neighbor pixels are very weak, and the higher order parameters, whih would not play an important role in texture reognition, will be abandoned by means of feature seletion (desribed in Setion II-B). The proposed SSLS algorithm is omputationally more eonomial, wherein PS-GMRF texture features are alulated in groups, thereby obviating the large matrix proess in the lassial LS method. B. Texture Feature Seletion A set of texture features having good disriminating power is essential for a texture-image-lassifiation system. The presene of a large number of texture features ould lead to poor 3 In the traditional way, model order is one of the important onerns for a suessful texture lassifiation. Usually, the more ompliated the texture, the higher order model should be used. Kashyap and Chellappa [6] presented a method generated from Bayes proedure for estimation and hoie of the method order for the GMRF texture.

4 ZHAO et al.: CLASSIFICATION OF HIGH SPATIAL RESOLUTION IMAGERY 1461 lassifiation results if are is not given to the ontribution of these features to the interlass separability [16]. In order to redue data dimensions and improve lassifiation quality, GMRF features extrated from the input image should be proessed by feature seletion. The problem of feature subset seletion is to selet a subset of b features from a larger set of a (b <a) features to optimize the value of a riterion over all subsets of the size b. There are ( a b) = (a!/b!(a b)!) suh subsets, for example, ( ) 11 3 = (11!/3!8!) = 165 and ( ) 14 3 = (14!/3!11!) = 364. Exhaustive evaluation of all the subsets is omputationally prohibitive. To avoid the exhaustive searh for an optimal feature subset, diret methods of seleting a suboptimal feature subset instead of searhing for an optimal subset have been developed [17], [18], suh as sequential forward seletion (SFS), sequential bakward seletion (SBS), plus-l take-away-r (PTA), sequential forward floating seletion (SFFS), and sequential bakward floating seletion (SBFS). SFS (SBS) begins with a feature subset and sequentially adds (removes) features until the termination riterion is met. Both of them suffer from the so-alled nesting effet, whih implies that for SFS the features seleted annot be removed, and for SBS, the features disarded annot be reseleted later. PTA was proposed to prevent the nesting effet, repeating this proess: Go forward l stages by adding l features by SFS and go bakward r stages by deleting r features by SBS. However, there is no theoretial guidane to determine the appropriate value of l and r. SFFS and SBFS are the floating version of PTA [19]. SFFS an be understood as plus one minus x and minus one plus x, where x is dynamially hanged aording to the baktrak effet. Unlike PTA, SFFS an baktrak unlimitedly as long as the baktrak finds a better feature subset than the feature subset obtained so far at the same size. Moreover, SBFS is the bakward version of SFFS. In this paper, the SFFS method is adopted in view of its good performane in both the quality of obtained feature subset and omputation effiieny. Texture features are evaluated aording to their disriminatory power. The riterion used here for feature seletion is the Kappa oeffiient of testing samples, based on the Gaussian maximum-likelihood lassifier [20]. III. CLASSIFICATION METHODOLOGY Maximum a posterior (MAP) estimation is known to be optimal in the sense that it is a probabilisti relaxation method as a means to enfore the spatial onstraints into lassifiation. The fundamental notion of spatial ontext is that the lass attributions of two spatially adjaent pixels are highly related. For example, if s and s are two neighboring pixels and if s belongs to lass, then there is a high probability that pixel s also belongs to the same lass. Thus, lassifiation should be arried out based not only on feature values of eah pixel but also on the statuses of the orresponding pixels. An input multidimensional feature image to be lassified is desribed by y = {y 1, y 2,...,y w }, where w is the number of pixels in the input image and y i =(yi 1,y2 i,...,yd i )T represents a vetor of d features (an be spetral and texture features) for the ith pixel. Assuming that there are C predetermined lasses in the input feature image, the orresponding lassified image an be denoted by l = {l 1,l 2,...,l w }, where l i takes on value in {1, 2,...,C}, and eah pixel is assigned to one of the C lasses aording to predefined rules. The MAP estimator is to maximize the following expression [21]: p(l y) = p(y l)p(l) p(y) (11) where p(l y) is the posteriori probability of l onditioned on y, p(y l) denotes the likelihood distribution of y onditioned on l, p(l) is the aprioriprobability of l, and p(y) is the probability distribution of y. As p(y) is normally onsidered as onstant, maximizing (11) is equivalent to maximizing the produt of p(y l) and p(l). In this paper, p(y l) is modeled in terms of the Gaussian distribution, while p(l) is modeled based on the seond-order multilevel logisti (MLL) model. When MAP estimation is applied to image lassifiation, the task beomes a ombinatory optimization problem [22]. A deterministi relaxation algorithm, iterated onditional modes (ICM) [23], is used beause of its omputational effiieny. The ICM solution is obtained by performing the following optimization. Moreover, for the ith pixel, the lass attribute is determined by l i = arg max {p(y i )p ( l j,j V (i))} (12) {1,2,...C} where V (i) denotes the eight-onnetivity neighbor set of the ith pixel. The law p(y i ) is given by [ ] 1 p(y i )= (2π)d exp 1 2 (y 1 i µ ) T (y i µ ) (13) where µ is the mean vetor and Σ is the ovariane matrix assoiated with the lass. The hosen aprioriterm is an MLL model, whih yields p ( l j,j V (i)) = 1 Z exp αδ(, l j ) (14) j V (i) where α is the model parameter expressing the strength of how an ourrene of lass for pixel i is affeted by its neighbor lass attributes {l j,j V (i)}, Z is a normalizing onstant, and δ(, l j ) is an indiator funtion, if = l j, δ(, l j )= 1, otherwise, δ(, l j )=1. In fat, the variation of α does not greatly influene the results [24], and α is taken as 1.0 in our experiments. Then, using (13) and (14) and after some manipulations, (12) is equivalent to { 1 l i = arg min {1,2,...C} 2 (y 1 i µ ) T (y i µ ) ln + } αδ(, l j ). (15) j V (i) Equation (15) is to ahieve the minimum of the entire energy, whih ould be onsidered as the sum of the energy of the

5 1462 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 5, MAY 2007 Fig. 3. QuikBird image mosai of the six sampled land-over lasses. Fig. 2. MAP ICM lassifiation algorithm flow hart. observed field (the first term plus the seond term) and the energy of the labeled field (the third term). The lassifiation method based on the MAP riterion using the ICM update sheme, named as the MAP ICM algorithm, is implemented by the steps outlined below. The method is desribed by a flow hart in Fig. 2. Step 1) Estimate the statisti parameters (µ, Σ ) for eah lass. Step 2) Based on (µ, Σ ), estimate an initial lassifiation using the nonontextual pixelwise maximumlikelihood deision rule by minimizing the energy of the observed field of (15). Step 3) Update (µ, Σ ) by µ = 1 y i I(l i,) (16) n and i = 1 (y i µ n )(y i µ ) T I(l i,) (17) i where n is the number of pixels in lass ; I(l i,)= 1, ifpixeli belongs to lass and zero otherwise. Step 4) For eah pixel of the image, perform the loal minimization defined by (15) and update the orresponding label in parallel. Step 5) Go bak to Step 3) until the speified determination ondition is satisfied. In this paper, we take the maximum iteration of 80 as the stopping riterion. Note that in Step 1), for the supervised lassifiation, (µ, Σ ) is omputed from the training samples of eah lass, while for the unsupervised lassifiation (µ, Σ ) is obtained by the fuzzy C-means (FCM) algorithm [25]. Supposing a partition matrix U =[u i ], where u i represents the probability for the pixel i to be in the luster, µ and Σ are estimated from the u i w i=1 µ = u2 i y i w (18) i=1 u2 i and = w i=1 u2 i (y i µ )(y i µ ) T w. (19) i=1 u2 i IV. CLASSIFYING LAND-COVER TYPES USING QUICKBIRD IMAGERY In this paper, we onsider a QuikBird true-olor image with 2.4-m spatial resolution of a suburban area in Beijing, China. Fig. 3 is a mosai of six different land-over types seleted from the QuikBird image, oded as C1 C6 from left to right and then up to bottom. C1 is the residential distrits made of onrete, in whih houses are arranged orderly. C2 and C3 are woodlands with different density and have similar spetral harateristis; C2 is sparse and fine in texture, while C3 looks very thik and exhibits large partiles in texture. C4, C5, and C6 are three different kinds of rops. From the point of view of refletive spetral values, C5 is different from C4 and C6, and C4 is quite like C6. Moreover, in terms of spatial strutures, C5 is oarsest, and C6 is finest. The size of eah image hip is The lassifiation sheme outlined in Fig. 4 was used for lassifying different lasses of the QuikBird image. First, band 1 (B-band) of QuikBird data was hosen for omputing texture features, beause this band has the maximum variability in terms of standard derivation. Texture features are extrated based on a moving window of a fixed size. We attempt to find a window that is not too large in order to limit omputation time and avoid the influene of the texture features of adjaent different types; on the other hand, the window should not be too small, as otherwise a robust GMRF estimator would not be obtained [26]. The pixels were used in the experiments as a result of a tradeoff between the appliation of too large and too small a window, and the odd number makes sure that the window is entered on the urrent pixel. Using a fourth-order GMRF model and a program written in the C++ language, a 14-D PS-GMRF feature vetor is yielded for eah pixel of the input image. All experiments in this paper are performed using the omputer with Intel Pentium proessor 1.60-GHz and EMS

6 ZHAO et al.: CLASSIFICATION OF HIGH SPATIAL RESOLUTION IMAGERY 1463 Fig. 6. CGMRF texture features and the orresponding lassified image. (a) False-olor image omposed of the optimal three CGMRF texture features: ˆβ(0, 1), ˆβ(1, 1), and ˆν, extrated from the QuikBird texture mosai. (b) Classified image using the ombined spetral and seleted CGMRF texture data. Fig. 4. Flow diagram for lassifying the QuikBird image mosai. Fig. 5. PS-GMRF texture features and the orresponding lassified image. (a) False-olor image omposed of the optimal three PS-GMRF texture features: ν (1), ν (2),andν (3), extrated from the QuikBird texture mosai. (b) Classified image using the ombined spetral and seleted PS-GMRF texture data. memory of 512 MB. The omputational time for PS-GMRF texture feature extration from Fig. 3 is 133 s. All the extrated texture features are normalized so that the measurements have zero mean and unit standard deviation. Afterwards, SFFS is implemented and the seleted PS-GMRF texture features are ν (1), ν (2), and ν (3). Fig. 5(a) shows the false-olor images omposed of the above features. Finally, the MAP ICM lassifier designed in Setion III is applied to the QuikBird spetral bands ombined with all the seleted PS-GMRF features together, in whih the initial lassifiation is obtained by a maximum-likelihood tehnique. The lassified image is depited in Fig. 5(b). For omparison, the 11-D CGMRF feature vetors were also omputed from band 1 of the QuikBird original image and tested aording to the flow hart expressed by Fig. 4. The omputation time for CGMRF texture feature extration from Fig. 3 is 191 s, more than the 133 s for PS-GMRF. After feature seletion, the seleted CGMRF features are ˆβ(0, 1), ˆβ(1, 1), and ˆν, whih are displayed in Fig. 6(a). Likewise, the lassifiation sheme using spetral information inorporated the seleted CGMRF texture information was employed to lassify land-over types, and Fig. 6(b) is the orresponding result. From Figs. 5 and 6, it is obvious that the PS-GMRF texture features perform better than CGMRF texture features. Sine a preise ground truth for the QuikBrid texture mosai is available, the performane of the two types of texture features an be exatly evaluated in a quantitative manner. Overall auray and Kappa oeffiient, whih are proven to be general and robust for any lassifiation algorithm (pixel-based lassifiation methods, neighbor/region-based lassifiation methods [27], [28], or objet-based lassifiation methods), are ated as riteria to summarize lassifiation results [29]. Inorporating PS-GMRF features, a Kappa value of was obtained with an overall auray of 94.01%, while when inorporating CGMRF features, the Kappa oeffiient was only and the overall auray is 71.84%. In order to further demonstrate that PS-GMRF features are superior to those of CGMRF, for eah type of QuikBird samples in Fig. 3, 2000 samples were hosen to analyze the distribution of both. For PS-GMRF texture features, the histograms of ν (1), ν (2), and ν (3) are plotted in Fig. 7(a) (), respetively. Eah graph onsists of the histograms of only one feature for all the six land-over types C1 C6, simultaneously. The average profile of the three seleted PS-GMRF features is shown in Fig. 7(d). In the same way, the histograms of the seleted CGMRF texture features: ˆβ(0, 1), ˆβ(1, 1), and ˆν are shown in Fig. 8(a) () and the average profile in Fig. 8(d). A good feature should have minimal intralass distane and maximum interlass distane. The histograms in Fig. 7 are approximately detahed, while those in Fig. 8 almost oinide for the texture types. Obviously, PS-GMRF features are more disriminatory than CGMRF features. Moreover, the transformed divergene (TD) measures between lass pairs of Fig. 3 are given in Table I(a) and (b), using the seleted PS-GMRF and CGMRF features, respetively. TD values vary from 0.0 to 2.0 orresponding to omplete overlap and ideal separation between two lasses. For the seleted PS-GMRF features, the overall average of TD values among lasses was 1.99, whereas it dropped to 1.31 for the seleted CGMRF features. Next, in order to evaluate the ontribution of the integration of the spetral and texture features in the disrimination of the six land-over types, the spetral data and the texture data were, respetively, used alone to lassify the QuikBird texture mosai. The omparison results are listed for five ombinations in Table II: 1) using spetral features alone; 2) using the seleted CGMRF texture features alone; 3) ombining the spetral and seleted CGMRF features; 4) using the seleted PS-GMRF texture features alone; and 5) ombing the spetral and seleted PS-GMRF features. From this table, two valuable observations

7 1464 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 5, MAY 2007 Fig. 7. (a) () Histograms of the seleted PS-GMRF features: ν (1), ν (2),andν (3). Eah of them orresponds to the distribution of one feature for six different texture types of Fig. 3. (d) Average profile of the seleted PS-GMRF features. Fig. 8. (a) () Histograms of the seleted CGMRF features: ˆβ(0, 1), ˆβ(1, 1),andˆν. Eah of them orresponds to the distribution of one feature for six different texture types of Fig. 3. (d) Average profile of the seleted CGMRF features. TABLE I TD MEASURES BETWEEN QUICKBIRD CLASS PAIRS USING THE SELECTED TEXTURE FEATURES (a) PS-GMRF AND (b) CGMRF TABLE II CLASSIFICATION RESULTS FOR THE QUICKBIRD IMAGE MOSAIC USING DIFFERENT TYPES OF FEATURES V. R ESIDENTIAL AREA EXTRACTION USING LOW-ORDER VARIANCE an be made. One is that PS-GMRF texture features provide better disrimination than CGMRF texture features. The Kappa oeffiient inreased when using the seleted PS-GMRF features (0.8474) ompared to lassifiation using the seleted CGMRF features (0.3026). The other is that the integrated utilization of spetral and texture features an obtain a more satisfatory result than either spetral or texture features alone. The Kappa oeffiient was using spetral data alone, using the seleted PS-GMRF texture data alone, and using the ombination of spetral and seleted PS-GMRF texture data. Residential areas are haraterized by low orrelation and high variane, whih annot be aurately deteted by spetral data. It was found that one of the PS-GMRF texture features, the lowest order variane ν (1), is effetive for residential area extration. The following introdues the experiments using the IKONOS and SPOT-5 images. These images were provided by the Spae Imaging Corporation and the Satellite Imaging Corporation. A. Experiment on IKONOS Imagery Fig. 9(a) shows a true-olor image olleted by the IKONOS satellite and is situated at the Brazil/Paraguay border. This image onsists of three senes of 350 lines, eah 400 pixels in length. When using the spetral features alone, the lassified image was obtained with the MAP ICM algorithm, as shown in Fig. 9(b). The initialization was proessed by means of FCM, in whih the number of lusters was fixed at five beforehand. From

8 ZHAO et al.: CLASSIFICATION OF HIGH SPATIAL RESOLUTION IMAGERY 1465 Fig. 9. (a) Initial IKONOS image. (b) Classified result of (a) using the spetral data. Fig. 11. (a) Overlap image between the deteted residential areas (in white lines) and the original IKONOS image. (b) Final lassified result of Fig. 9(a) after ombining deteted residential areas. TABLE III CODES ABOUT FIVE TYPES OF LAND COVERS IN FIG. 11(b) Fig. 10. PS-GMRF texture features extrated from Fig. 9(a). (a) Feature image of ν (1),(b)ν (2),()ν (3),and(d)ν (4). Fig. 9, it is obvious that the spetrally heterogeneous residential areas, whih are omposed of some small subjets suh as building and green plants, annot be deteted as an entire objet using spetral features, while spetrally homogenous land overs, suh as sea and sandy land areas, an be lassified well with only spetral information. In order to redue the blurring border effet, inherent in texture analysis and whih introdues important errors in the transition areas between different texture units [12], a lassifiation proedure was designed for suh satellite images as follows. First, residential areas are deteted using texture features; afterward, other objets are lassified using spetral features. Based on the fourth-order GMRF model, PS-GMRF texture features were omputed from the one band with the largest spetral variability. Fig. 10 displays the results of the extration of four PS-GMRF features: ν (1), ν (2), ν (3), and ν (4). It is easy to see that the lowest order variane ν (1) has a larger apaity for disriminating between residential areas and other land-over types. Two lusters, one a residential area and the other not, were deteted in the lowest order variane spae using FCM. Sequentially, a mask was generated to elim- inate residential areas so that other land-over types would be lassified aurately, and not interfered with the spetrally heterogeneous residential areas. The MAP ICM lassifiation algorithm under the mask was performed on the input data in Fig. 9(a), followed by ombining the resident map, as shown in Fig. 11(a). Fig. 11(b) is the final lassified result. Comparing Figs. 9(b) and 11(b), it is obvious that the performane of lassifiation in residential areas was improved by joining texture features with spetral features. In order to prove the effetiveness of the lowest order variane for residential-area detetion in a quantitative manner, omparisons of disriminative abilities of fourth-order varianes (ν (1), ν (2), ν (3), and ν (4) ) are provided based on the lassifiation result of Fig. 11(b). For the sake of simpliity, five different types of land overs are oded. The residential area labeled in white in Fig. 11(b) is denoted as D1, and other land overs are numbered by D2 D5 given in Table III. Just similar to Setion IV, 2000 samples for eah kind of land overs were seleted aording to the final lassified image in Fig. 11(b), and histograms were plotted to analyze distributions of the fourth-order varianes orresponding to eah land-over type, shown in Fig. 12. All of PS-GMRF texture features were normalized to have zero mean and unit standard deviation. Fig. 12 ontains four graphs (a) (d), orresponding to ν (1), ν (2), ν (3), and ν (4), respetively. Eah graph has five urves of histogram statistis and eah urve represents the distribution of one feature with regard to one type of land overs. In Fig. 12, the graph (a) has the largest gaps between D1 (residential areas) and D2 D5 (other types) and no overlapped area between them. It is not diffiult to find that the first-order variane ν (1) has the higher ability to disriminate between residential areas and other land overs than those higher order varianes, suh as ν (2) ν (4).

9 1466 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 45, NO. 5, MAY 2007 Fig. 12. Histograms of four varianes extrated from Fig. 9(a). (a) Histograms of ν (1),(b) ν (2),() ν (3),and(d) ν (4). Eah image onsists of five urves, and eah urve orresponds to the distribution of one texture feature for one land-over types of Fig. 11(b). Fig. 14. PS-GMRF texture variane features extrated from Fig. 13(a). (a) Feature image of ν (1),(b)ν (2),()ν (3),and(d)ν (4). Fig. 13. (a) Initial SPOT-5 data. (b) Classified result of (a) using the spetral information. B. Experiment on SPOT-5 Imagery Subsequent experiments were made on a SPOT-5 image. Fig. 13(a) is part of the SPOT-5 satellite image of Reno, NV, aquired on September 14, 2003, and the image size is Using the spetral features, the MAP ICM lassifiation with FCM was arried out to detet five lusters. The result is shown in Fig. 13(b), and residential areas are not separated aurately from other land-over types. Residential areas with high intralass spetral variane would be deteted exatly when texture features are onsidered. The band with the largest spetral variability was seleted from Fig. 13(a) and PS-GMRF texture features omputed with the fourth-order GMRF model. Fig. 14 shows the PS-GMRF varianes: ν (1), ν (2), ν (3), and ν (4). In Fig. 14(a), the values are high for pixels inside the residential areas, whih are haraterized by high lowest order variane, while the values are low for pixels in other land overs, suh as fields and water areas, whih are haraterized by small lowest order variane. To avoid the adverse effet of blurring at the boundaries, residential areas were first extrated Fig. 15. (a) Overlap image between the deteted residential areas (in white lines) and the original SPOT-5 image. (b) Final lassified result of Fig. 13(a) after ombining deteted residential areas. aording to the lowest order variane. White lines outline the deteted residential areas in Fig. 15(a). After masking the residential areas, the MAP ICM proedure is proessed in Fig. 13(a), in whih FCM is used to initialize four lustering enters. By integrating the above two results, the final lassified image was ahieved, as shown in Fig. 15(b). The proposed lassifiation approah improves the lassifiation auray and visual interpretation, ompared to lassifiation with purely spetral features. Subsequently, apabilities of residential-area extration are presented for different order varianes to rededue the above onlusion that the lowest order variane for the detetion of residential areas is more effetive than the higher order varianes, based on the lassifiation result. Five land-over types are numbered E1 E5, as listed in Table IV. E1 represents residential areas, E2 E5 as the other land overs. In the

10 ZHAO et al.: CLASSIFICATION OF HIGH SPATIAL RESOLUTION IMAGERY 1467 TABLE IV CODES ABOUT FIVE TYPES OF LAND COVERS IN FIG. 15(b) Fig. 16. Histograms of four varianes extrated from Fig. 13(a). (a) Histograms of ν (1),(b)ν (2),()ν (3),and(d)ν (4). Eah image onsists of five urves, and eah urve orresponds to the distribution of one texture feature for one land-over types of Fig. 15(b). same way, for eah type of land over, 2000 samples seleted, aording to the lassified image Fig. 15(b), were used for statistial alulation, sequentially the distribution urves of eah order variane were given in Fig. 16, where Fig. 16(a) (d) orresponding to ν (1), ν (2), ν (3), and ν (4), respetively. Moreover, in Fig. 16(a), there are five urves. Eah urve depits the distribution of the lowest order variane ν (1) of eah kind of land overs, and the urve of E1 (residential areas) is apart from the other four urves of E2 E5 (other land overs). However, in Fig. 16(b) (d), five urves overlap in large areas. Hene, it is established that the lowest order variane is more effetive for residential area extration than higher order varianes. VI. CONCLUSION Traditional per-pixel lassifiation methods based upon spetral omparisons are not effiient for spatially heterogeneous land-over and land-use lasses in high spatial resolution imagery. The effetiveness of the spatial information has been tested via texture analysis based on the GMRF model to improve lassifiation auray. This paper has developed a new set of GMRF texture features: PS-GMRF features. CGMRF features are omputed when all the neighboring pixels are onsidered at the same time and treated equally. PS-GMRF features are derived when neighboring pixels are taken into aount in a priority sequene dependent on their distane from the enter pixel. A omplete proedure has been designed to lassify texture samples from high spatial resolution satellite imagery. First of all, PS-GMRF texture features are extrated. Then, SFFS is applied to obtain an optimal subset of PS-GMRF features. The seleted PS-GMRF texture features, in ombination with spetral features, are given as an input to the MAP ICM lassifier. The ase study on the QuikBird image mosai shows that a ombination of texture and spetral features signifiantly improves the lassifiation auray ompared to lassifiation with spetral features only. The Kappa oeffiient inreased from to with the addition of the seleted PS- GMRF texture features. On the other hand, CGMRF features are used instead of PS-GMRF features for omparison. The Kappa oeffiient inreased from to for an addition of the seleted CGMRF texture features. It is obvious that PS-GMRF features are superior to CGMRF features. Furthermore, one of the PS-GMRF texture features the lowest order variane proved to be valid for extrating residential areas from high spatial resolution satellite imagery. In order to avoid the blurring-border effet due to texture features and the disturbane with spetrally heterogeneous residential areas, spetrally homogeneous objets were lassified after residential-area detetion, based on the lowest order variane. The MPA ICM lassifier was arried out, in whih MFC is used for initialization. Some experimental results on IKONOS and SPOT-5 images demonstrated that the proposed method outperforms in terms of visual evaluation, ompared with lassifiation with spetral features alone. ACKNOWLEDGMENT The authors would like to thank the Spae Imaging Corporation for providing the IKONOS image and the Satellite Imaging Corporation for providing the SPOT-5 image. REFERENCES [1] S. P. Lennartz and R. G. Congalton, Classifying and mapping forest over types using IKONOS imagery in the northeastern United States, in Pro. ASPRS Annu. Conf., Denver, CO, [Online]. Available: [2] S. E. Franklin, R. J. Hall, L. M. Moskal et al., Inorporating texture into lassifiation of forest speies omposition from airborne multispetral images, Int. J. Remote Sens., pp , vol. 21, no.1, Jan [3] T. R. Reed and J. M. H. Du Buf, A review of reent texture segmentation and feature extration tehniques, CVGIP: Image Underst.,vol.57,no.3, pp , May [4] P. P. Ohanian and R. C. Dubes, Performane evaluation for four lasses of textural features, Pattern Reognit., vol. 25, no. 8, pp , [5] D. A. Clausi and H. Deng, Design-based texture feature fusion using Gabor filters and o-ourrene probabilities, IEEE Trans. Image Proess., vol. 14, no. 7, pp , Jul [6] R. L. Kashyap and R. Chellappa, Estimation and hoie of neighbors in spatial-interation models of images, IEEE Trans. Inf. Theory,vol.IT-29, no. 1, pp , Jan [7] R. Chellappa and S. Chatterjee, Classifiation of textures using Gaussian Markov random fields, IEEE Trans. Aoust., Speeh, Signal Proess., vol. ASSP-33, no. 4, pp , Aug [8] M. Datu, K. Seidel, and M. Walessa, Spatial information retrieval from remote-sensing images Part I: Information theoretial perspetive, IEEE Trans. Geosi. Remote Sens., vol. 36, no. 5, pp , Sep

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Inst., 2005, pp Yindi Zhao reeived the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in Her urrent interests are in model-based texture analysis and pattern reognition, with appliations in high spatial resolution remote sensing. Liangpei Zhang (M 06) reeived the B.S. degree in physis from Hunan Normal University, ChangSha, China, in 1982, the M.S. degree in optis from the Xi an Institute of Optis and Preision Mehanis, Chinese Aademy of Sienes, Xi an, in 1988 and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in From 1997 to 2000, he was a Professor with the Shool of the Land Sienes, Wuhan University. In August 2000, he joined the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, as a Professor and Head of the Remote Sensing Setion. He has published more than 120 tehnial papers. His researh interests inlude hyperspetral remote sensing, high-resolution remote sensing, image proessing, and artifiial intelligene. Dr. Zhang has served as the Cohair of the International Soiety for Optial Engineers Series Conferenes on Multispetral Image Proessing and Pattern Reognition (MIPPR) and the Conferene on Asia Remote Sensing in 1999, as an Editor of the MIPPR 01, MIPPR 05, and Geoinformatis Symposiums, as an Assoiate Editor of Geo-spatial Information Siene Journal, as a member of the Chinese National Committee for the International Geosphere-Biosphere Programme, and as an Exeutive Member for China Soiety of Image and Graphis. Pingxiang Li (M 06) reeived the B.S., M.S., and Ph.D. degrees in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 1986, 1994, and 2003, respetively. Sine 2002, he has been a Professor with the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University. His researh interests inlude photogrammetry and SAR image proessing. Bo Huang reeived the Ph.D. degree in geographi information system from the Institute of Remote Sensing Appliations, Chinese Aademy of Sienes, Beijing, China, in He is urrently an Assoiate Professor with the Department of Geography and Resoure Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong. His urrent researh interests inlude spatial optimization, spatial statistis for hange analysis, and image proessing. He has authored and oauthored over 30 papers in refereed international journals and developed several GIS, spatial analysis, and optimization software pakages.