4178 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 9, NO. 9, SEPTEMBER 2016

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1 4178 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 9, NO. 9, SEPTEMBER 016 Hyperspectral Image Classification by Fusing Collaborative and Sparse Representations Wei Li, Member, IEEE, QianDu,Senior Member, IEEE, Fan Zhang, Member, IEEE,andWeiHu Abstract This paper proposes to combine collaborative representation (CR) and sparse representation (SR) for hyperspectral image classification. SR may select too few samples that cannot well reflect within-class variations, while CR generates nonsparse code using all the atoms that may unfortunately include betweenclass interference. To alleviate these problems, two methods fusing CR and SR are proposed, i.e., a fused representation-based classification (FRC) method and an elastic net representation-based classification (ENRC) method. FRC attempts to achieve the balance between CR and SR in the residual domain, while ENRC uses a convex combination of l 1 and l penalties. Experimental results on two hyperspectral data demonstrate that the proposed methods outperform the original counterparts, i.e., CR-based classification (CRC) and SR-based classification (SRC). Index Terms Classifier fusion, collaborative representation (CR), hyperspectral classification, sparse representation (SR). I. INTRODUCTION H YPERSPECTRAL imagery consists of hundreds of narrow contiguous wavelength bands that include detailed spectral information about materials in an image scene. Taking advantage of the rich spectral information, numerous classification and detection algorithms using hyperspectral data have been developed for a variety of applications [1] [6], such as land cover, land use mapping, and environmental monitoring. In statistical pattern classification, a simple assumption is usually made that data abide by a normal or multimodal distribution. Thus, popular choices of classifiers are a single- Gaussian maximum-likelihood classifier (MLC) and Gaussianmixture-model (GMM) classifier [7], [8]. However, a single- or multiple-gaussian distribution may not be true under small training sample size (SSS) situations, which often happens in remotely sensed hyperspectral imagery [9], [10]. Manuscript received August 09, 015; revised January 18, 016; accepted March 10, 016. Date of publication April 10, 016; date of current version September 30, 016. This work was supported in part by the National Natural Science Foundation of China under Grants No. NSFC , , and in part by the Fundamental Research Funds for the Central Universities under Grants No. BUCTRC01401, YS1404, XK151. W. Li, F. Zhang, and W. Hu are with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 10009, China ( liwei089@ieee.org; zhangf@mail.buct.edu.cn; huwei@mail.buct.edu.cn). Q. Du is with the Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 3976 USA ( du@ece.msstate.edu). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /JSTARS Recently, representation-based classification, which does not assume any data density distribution, has gained great interest [11] [14]. The principle behind such classification is that a testing pixel can be linearly represented by labeled samples. The weight coefficients are solved by an l 0 -norm or l 1 -norm penalty, named as sparse representation (SR), or an l -norm penalty, denoted as collaborative representation (CR). SR-based classification (SRC) [15] was originally developed for face recognition. The essence of SRC is built on the concept that a testing pixel can be sparsely represented as a linear combination of the labeled data via l 0 -norm or l 1 -norm regularization. It does not require a training process, which is obviously dissimilar to the training-testing fashion in conventional classifiers (e.g., support vector machine [16]). In SRC, the class label of a testing pixel is determined to be that of the class whose labeled samples provide the smallest approximation error. In [17], kernel version of SRC (KSRC), representing the data in a high-dimensional kernel-induced feature space, was presented. In [11], SRC was applied to hyperspectral image classification, and demonstrated excellent performance under SSS situations. In [18], KSRC using spatial-spectral features was further discussed for hyperspectral image classification. It has been argued that it is the collaborative nature of the approximation instead of competitive nature imposed by sparseness constraint that improves classification accuracy [19], [0]. CR means all atoms collaborate on representation of a single pixel, and each atom has the equal chance to participate. It is solved with an l -norm regularized least squares formulation. In [1], the original CR-based classification (CRC) was extended to the case where samples were weighted using the locality assumption, denoted as WCR. In [], joint within-class CRC was presented, which approximated the testing samples using class-specific labeled samples separately instead of using all the labeled data. Kernel-based CRC (KCRC) was further investigated for hyperspectral image classification in [3]. Nonlocal joint CR with a locally adaptive dictionary was developed in [4], and Gabor feature-based CR was discussed in [5]. Note that l 1 -minimization-based SR and l -minimizationbased CR are known as LASSO and Ridge methods, respectively, and both have certain limitations [6]. One drawback of SR is that it tends to select only one or two atoms from highly corrected samples because of the nature of convex optimization [1], which causes weight coefficients too sparse and the final residual may be biased. In particular, when the number of labeled samples is relatively small, the corresponding sparsity of combinational coefficients will become so weak that classification performance is deteriorated. In CRC, weight coefficients IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.

2 LI et al.: HYPERSPECTRAL IMAGE CLASSIFICATION BY FUSING CR AND SR 4179 are not required to be sparse, and its discriminant ability is limited when labeled samples include mixed-class information. Intuitively, more atoms should be selected to reflect withinclass variations instead of selecting very few atoms as in SR, and nonsparse coefficients should be allowed only to selected atoms to eliminate between-class interference rather than allowing all the atoms to participate in the representation as in CR. Thus, in this work, we propose a fused representation-based classification (FRC) method to achieve the balance between CR and SR in the residual domain; furthermore, we also propose to use an elastic net representation-based classification (ENRC) method to combine l 1 and l penalties in the objective function for grouping selection of highly correlated data so that more robust weight coefficients can be estimated for better classification performance. Actually, information fusion or multiclassifier fusion has been widely studied to combine information to achieve an improved performance. In [7], feature-level fusion was applied to concatenate multiple features (i.e., local binary patterns, Gabor features, and spectral features) before pattern classification. In [8] and [8], a combination of several classifiers in decision-level fusion has been discussed. In [9], a CR optimized classifier (CROC), which builds a weighted combination of CRC and nearest subspace classifier in the residual domain, was presented. In our research, the proposed FRC accomplishes a balance between CR and SR via adopting a proper weighted combination in the residual domain. On the other hand, previous research [30], [31] has affirmed that an elastic net model can offer more robust and sparse coefficients. In [30], the model was employed to obtain satisfied SR for super-resolution. In [31], the model was applied for enhanced face recognition under noisy environment. In this work, ENRC is employed to overcome the indigenous disadvantages of SRC and CRC. It is expected that the fused residual from FRC and the weight vector from ENRC reveal more powerful discriminant ability thereby outperforming the original SRC and CRC. The remainder of this paper is organized as follows. Section II details the proposed fusion-based classification framework, including ENRC and FRC. Section III presents the hyperspectral data and parameter tuning as well as experimental results. Finally, Section IV makes several concluding remarks. II. FUSED REPRESENTATION-BASED CLASSIFICATION Consider a dataset with training samples X = {x i } n i=1 Rd (d is the number of spectral bands) and class labels ω i {1,,...,C}, where C represents the number of classes, and n is the total number of training samples. Let n l be the number of training samples for the lth class, and C l=1 n l = n. A. SRC and CRC An approximation of a testing sample y is represented via a linear combination of all available labeled training data, X. In SRC, for a testing sample y, the objective of SR is to find weight vector α (SR) for the linear combination such that y Xα (SR) is minimized with sparse constraint term α (SR) 1. So, the objective function can be formulated as y arg min Xα (SR) α (SR) + λ 1 (1) α (SR) 1 where λ 1 is a regularization parameter. The weight vector α (SR) in (1) can be solved by the basis pursuit (BP) method [3] or basis pursuit denoising (BPDN) algorithm [33]. If l 0 - norm is directly used, then the problem can be approximately solved by greedy pursuit algorithms, such as orthogonal matching pursuit (OMP) [34] or subspace pursuit (SP) [35]. Here, the l 1 -norm penalty minimization 1 can be implemented by mexlassoweighted.m. After obtaining α (SR), X and α (SR) are separated into l class-specific subdictionaries according to the given class labels of the training samples, i.e., {X l } C l=1 { } C and R nl 1. Class label of the testing R d n l α (SR) l l=1 sample is then determined according to the class that minimizes the residual between the class-specific approximation and the original pixel. That is rl SRC (y) = X l α (SR) l y () and class label SRC(y) =argmin l=1,...,c rl SRC (y). In CRC, the objective is to find the weight vector α (CR) for the linear combination such that y Xα (CR) is minimized under the constraint α (CR) is also minimized, which is expressed as y arg min Xα (CR) α (CR) + λ (3) α (CR) where λ is a regularized parameter. Taking derivative with regard to α (CR) and setting the resultant equation to zero yields α (CR) = ( X T X + λ I ) 1 X T y. (4) After obtaining α (CR), class label of the testing sample is then determined according to the minimum residual rl CRC (y). B. Proposed FRC In CRC, training samples collaboratively form the representation of a testing pixel. In other words, the representation can be viewed as a projection onto a subspace jointly spanned by the labeled samples. Similarly, the representation in SRC can be viewed as a projection onto a subspace sparsely spanned by the labeled samples, where only a few atoms are used. Recent literatures [], [3], [36] have compared the performance of CRC and SRC, and shown in some cases the classification improvement is brought by CR while in other cases the gain is brought by SR. Actually, CR means that all the atoms collaborate on the representation of a single pixel, and each atom has an equal chance to participate in the representation; on the contrary, SR reflects competitive nature imposed by sparseness constraint. For example, in a complex remotely sensing scenario, some 1

3 4180 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 9, NO. 9, SEPTEMBER 016 Fig. 1. One example (testing pixel from class 8) of how the proposed FRC affects the final classification using 50 training samples per class using the University of Pavia data (nine classes in total). (a) Weight coefficients for CRC. (b) Weight coefficients for SRC. (c) Residuals for CRC, SRC, and FRC. pixels, such as mixed pixels arise due to insufficient spatial resolution or other phenomena, are more suitably represented by SR [37], [38]. In order to make the representation more suitable, FRC can be applied as r FRC l (y) =(1 θ)rl CRC (y)+θrl SRC (y) (5) where θ is a balancing parameter (0 θ 1). The class label of the testing sample is determined according to the class with the smallest residual. When θ =0, the method reduces to CRC, and when θ =1, the method reduces to SRC. The overall description of FRC is summarized in Algorithm 1. To illustrate the benefits of FRC, Fig. 1 depicts an example about the weight coefficients of CRC and SRC using a testing pixel from the University of Pavia data to be introduced in Section III. In the example, 50 training samples per class are chosen and θ is set to 0.5. Fig. 1(c) shows the residuals of the testing pixel with respect to the training data of each class. In this example, the testing pixel is from class 8 (i.e., Bricks). From the comparative results, the minimum residuals of CRC and SRC are from class 3 (i.e., Gravel) and class (i.e., Meadows), respectively, which means the testing pixel is misclassified into either class or 3. The fused residual from FRC can recognize the correct class because the minimum residual Algorithm 1. Proposed FRC Classifier Input: Available training data X = {x i } n i=1, class labels ω i, and a testing sample y R d. Step 1: Determine the best parameters (i.e., λ 1, λ, and θ)by using cross-validation; Step : Obtain weight vector α (SR) in SRC according to Eq. (1); Step 3: Obtain weight vector α (CR) in CRC according to Eq. (4); Step 4: Calculate individual residuals rl SRC (y) and rl CRC (y) according to Eq. (); Step 5: Compute the fused residual rl FRC (y) according to Eq. (5); Step 6: Decide the final label class(y) = arg min l=1,...,c rl FRC (y). Output: class(y). corresponds to class 8. It is straightforward that a residual from the FRC is between those from SRC and CRC, and the value of θ is not critical as long as it is around 0.5 in the example. In particular, the FRC plays a critical role when the results of SRC

4 LI et al.: HYPERSPECTRAL IMAGE CLASSIFICATION BY FUSING CR AND SR 4181 Fig.. One example (testing pixel from class 3) of how the proposed ENRC affects the final classification using 50 training samples per class using the University of Pavia data (9 classes in total). (a) Weight coefficients for CRC. (b) Weight coefficients for SRC. (c) Weight coefficients for ENRC. (d) Residuals for CRC, SRC, and ENRC. and CRC are not consistent. This example verifies that residuallevel fusion (e.g., FRC) is more discriminative than SRC and CRC. C. Proposed ENRC In representation-based classification, the resulting weight coefficients carry a meaning since they reflect the importance of each training sample. Thus, the solution of the weight vector is the core part of this type of methods. For instance, sparse constraint in SRC shrinks the coefficients toward zeros if the corresponding samples are less relevant. Nevertheless, the sparsity of the weight coefficients becomes inaccurate and weak when the dictionary size is small. Fortunately, recent literatures [6], [31] [31] have pointed out that the elastic net model could efficiently avoid the problem via a convex combination of SR and CR, rendering robust coefficients. In this work, we employ the elastic net model in representation-based classification framework, named as ENRC. The objective function becomes y arg min Xα (EN) α (EN) α (EN) + λ 1 + λ. α (EN) 1 (6) When λ 1 =0, the method reduces to CRC, and when λ =0, the method reduces to SRC. In order to efficiently solve ENRC, an artificial dataset is first defined by X =(1+λ ) 1 ( ) X λ, y = I ( ) y 0 where X R (d+n) n and y R (d+n) 1. The ENRC actually tends to be an l 1 -norm minimization problem (7) arg min α y X α + λ 1 α 1. (8) 1+λ Note that α is a various representation of α (EN) with size of n 1. Above formulation can be solved similarly to (1). The weight vector is estimated by α (EN) = α 1+λ. (9) After obtaining α (EN), class label of the testing sample is then determined according to the minimum residual rl ENRC (y) similar to (). The processed on (8) can be proved as below. According to (7), we have X T X = XT X+λ I 1+λ, y T X =

5 418 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 9, NO. 9, SEPTEMBER 016 y T X 1+λ, and y T y = y T y. The formulation in (8) can be further expressed as y X α + λ 1 1+λ α 1 = α T (X T X )α y T X α +y T y + λ 1 α 1 1+λ ( ( X =(1+λ ) α (EN)T T ) ) X + λ I α (EN) 1+λ y T Xα (EN) α (EN) + λ 1 + y T y 1 = y Xα (EN) α (EN) α (EN) + λ 1 + λ. (10) 1 Fig. illustrates how the proposed ENRC improves the final classification. In Fig., a testing pixel is selected from class 3 (i.e., Gravel). If the weight coefficients are too dense, like CRC shown in Fig. (a), it is not reasonable since any contribution for approximation from other classes is not desired. On the other hand, if the weight coefficients are too sparse, like SRC shown in Fig. (b), the recovered residual shown in Fig. (d) may not be accurate or reliable. Here, the minimum residuals of CRC and SRC are both from class 8 (i.e., Bricks), which is incorrect. Thus, the ideal situation is that more corresponding nonzero coefficients are from the most relevant class. By combining l 1 -norm and l -norm regularized terms together in the objective function, elastic net representation actually makes use of both advantages of CR and SR, which guarantees grouping selection on highly correlated data and enforces the intrinsic sparsity as well as self-similarity of samples simultaneously. Different from the FRC, the ENRC may offer a correct label even when the decisions from SRC and CRC are consistently wrong. III. EXPERIMENTS AND ANALYSIS A. Experimental Data The first experimental data employed was acquired using National Aeronautics and Space Administration s (NASA) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and was collected over northwest Indiana s Indian Pine test site in June 199. The image represents a vegetationclassification scenario with pixels and 0 bands in the 0.4- to.45-µm region of the visible and infrared spectrum with a spatial resolution of 0 m. In this work, a total of 0 bands is used after removal of water-absorption bands (i.e., bands and ). There are 16 different land-cover classes in the original ground truth; however, eight classes are used in this study so as to avoid a few classes that have very few training samples [39], which are Corn-no till, Corn-min till, Grass/Pasture, Hay-windrowed, Soybean-no till, Soybeanmin till, Soybean-clean till, and Woods. There are 10 training samples per class randomly selected from the ground truth map, and rest samples are employed as testing (i.e., 1314, 714, 377, 369, 848, 348, 494, and 1174). biehl/multispec/hyperspectral.html The second experimental hyperspectral dataset 3 employed was collected by the Reflective Optics System Imaging Spectrometer (ROSIS) sensor. The image, covering the city of Pavia, Italy, was collected under the HySens project managed by DLR (the German Aerospace Agency). The data have a spectral coverage from 0.43 to 0.86 µm, and a spatial resolution of 1.3 m. The scene used is the university area which has 103 spectral bands with a spatial coverage of pixels. The nine classes used in our experiments are Asphalt, Meadows, Gravel, Trees, Metal Sheets, Bare Soil, Bitumen, Bricks, and Shadows. One hundred and twenty training samples per class are randomly selected from the ground truth map, and rest samples are employed as testing (i.e., 651, 18530, 1980, 945, 16, 4910, 111, 3563, and 88). B. Parameter Tuning We investigate the parameters of the proposed classification framework. As a regularized parameter, the adjustment of λ is important to the performance of representation-based classifiers. We report experiments demonstrating their sensitivity. In general, leave-one-out cross validation (LOOCV) strategy based on available training samples is considered for parameter tuning. Figs. 3(a) and 4(a) show the varying λ of the representation-based classifiers for the Indian Pines data and the University of Pavia data, respectively. Figs. 3(c) and 4(c) illustrate the parameter tuning for the proposed ENRC. In the University of Pavia data, both λ 1 and λ are set to 1e. In the Indian Pines data, the optimal λ 1 and λ can be 1e and 1e 3, respectively. Another important parameter for the proposed FRC is θ that balances the two residuals produced by CR and SR. Fig. 3(b) shows the performance of FRC with various θ for the Indian Pines data. It is clearly observed that when θ =0.4 or 0.6, the performance is much better than that when θ =0or 1, which indicates that fused residual is more discriminant than that of sole CR or SR. We obtain similar conclusion in Fig. 4(b) that provides the optimal θ for the University of Pavia data. All best parameters are used in following experiments. C. Classification Performance The classification performance of the proposed methods is summarized in Tables I II for the experimental data. Except of CRC and SRC, WCR in [1] is also employed for comparison. For both data, 10 training samples are randomly selected from the available ground truth map. To avoid any bias, we repeat the experiments 10 times and report the average classification accuracy. The accuracy for each class as well as the overall accuracy (OA) has been reported. It is worth mentioning that the same number of training samples per class is used since the performance of representation-based classifiers may be affected by unbalanced training dataset. For the Indian Pines data, the proposed FRC and ENRC can basically yield better classification performance than individual 3 Sensing_Scenes

6 LI et al.: HYPERSPECTRAL IMAGE CLASSIFICATION BY FUSING CR AND SR 4183 Fig. 3. Parameter tuning (e.g., λ and θ) of representation-based classifiers with 10 training samples per class for the Indian Pines data. (a) λ for CRC and SRC. (b) θ for the proposed FRC. (c) λ for the proposed ENRC. Fig. 4. Parameter tuning (e.g., λ and θ) of representation-based classifiers with 10 training samples per class for the University of Pavia data. (a) λ for CRC and SRC. (b) θ for the proposed FRC. (c) λ for the proposed ENRC. classifiers (i.e., CRC and SRC). In fact, for some classes (e.g., class 1 and class 3), SRC produces higher classification accuracy than CRC; while for other classes (e.g., class 5 and class 7), CRC is superior to SRC. WCR is generally between CRC and SRC. Nevertheless, the proposed FRC and ENRC basically outperform these three. All the results validate the motivation of our fusion-based classification strategies. In the University of Pavia data, it is still clearly to observe that FRC and ENRC outperform others. The standardized McNemar s test [40] has been employed to testify the statistical significance in accuracy improvement of the proposed FRC and ENRC. As listed in Table III, the z values of McNemar s test larger than 1.96 and.58 mean that two results are statistically different at 95% and 99% confidence levels. It further confirms performance improvement from the proposed FRC and ENRC. We also report ground-cover classification maps for these datasets Figs. 5 and 6 illustrate the thematic maps resulting

7 4184 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 9, NO. 9, SEPTEMBER 016 TABLE I CLASSIFICATION ACCURACY (%) PER CLASS AS WELL ASTHEOA WITH 10 TRAINING SAMPLES PER CLASS FOR THE INDIAN PINES DATA TABLE II CLASSIFICATION ACCURACY (%) PER CLASS AS WELL ASTHEOA WITH 10 TRAINING SAMPLES PER CLASS FOR THE UNIVERSITY OF PAVIA DATA TABLE III STATISTICAL SIGNIFICANCE FROM THE STANDARDIZED MCNEMAR S TEST ABOUT THE DIFFERENCE BETWEEN ALGORITHMS Fig. 5. Thematic maps resulting from classification with 10 training samples per class for the Indian Pines dataset. (a) False-color image. (b) Ground-truth map. (c) CRC. (d) SRC. (e) FRC. (f) ENRC. from the classification of these hyperspectral scenes using CRC, SRC, FRC, and ENRC. To facilitate performance comparison, only areas with available ground truth are shown. Clearly, FRC and ENRC result in classification maps that are less noisy and more accurate, especially compared to CRC. These maps are consistent with the results listed in Tables I II. Fig. 7 illustrates classification performance versus different numbers of training sample sizes for experimental data. For both data, the number of training samples per class varies from 40 to 10, and all the labeled pixels in the ground truth map are used as the testing data. It is apparent that the classification performance of FRC increases when the number of training samples increases, and is consistently better than that of CRC and SRC. Taking the Indian Pines data, for example, the gap between FRC and SRC is appropriate %. The ENRC can outperform FRC with a small number of training samples. Finally, the computational complexity of the aforementioned methods is reported in Table IV. All experiments were carried out using MATLAB on an Intel(R) Core(TM) i CPU machine with 8 GB of RAM. Note that the cost of ENRC is just slightly higher than that of SRC because their objective functions are similar [i.e., (8) for ENRC and (1) for SRC]. It is also worth mentioning that the l 1 -norm minimization of SRC in related toolbox of uses the MEX function which calls C program in MATLAB; otherwise, the computational cost should be much higher than CRC using the l -norm minimization. IV. CONCLUSION In this paper, fusion-based classification strategies (i.e., residual-level fusion and ENRC) were presented. The proposed FRC was designed to achieve a balance between CR and SR in the residual domain. It was found that the fused

8 LI et al.: HYPERSPECTRAL IMAGE CLASSIFICATION BY FUSING CR AND SR Classification accuracy (%) CRC SRC FRC ENRC Number of training samples per class (a) Classification accuracy (%) CRC SRC FRC ENRC Number of training samples per class (b) Fig. 7. Classification performance versus different numbers of training samples per class for the experimental data. (a) Indian Pines. (b) University of Pavia. TABLE IV EXECUTION TIME (IN SECONDS) IN THE TWO EXPERIMENTAL DATASETS Fig. 6. Thematic maps resulting from classification with 10 training samples per class for the University of Pavia dataset. (a) False-color image. (b) Groundtruth map. (c) CRC. (d) SRC. (e) FRC. (f) ENRC. residual has stronger discriminant ability than that produced solely by CR or SR, which is more suitable for representing a hyperspectral pixel in complex scenarios. As for the proposed ENRC, the combination of l 1 and l penalties in the objective function was proved to be capable of grouping selection on highly correlated data, producing more robust weight coefficients. Experimental results on real hyperspectral images verified that the proposed algorithms can outperform the existing representation-based classifiers. The ENRC may offer better performance than FRC when the number of training samples is small. Note that although the proposed FRC introduces an additional balancing parameter, its best value is within [ ] in the two experimental datasets. So, a simple value of 0.5 may achieve a satisfying performance without tuning.

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Remote Sens., vol. 49, no. 1, pp , Dec Wei Li (S 11 M 13) received the B.E. degree in telecommunications engineering from the Xidian University, Xi an, China, the M.S. degree in information science and technology from the Sun Yat-Sen University, Guangzhou, China, and the Ph.D. degree in electrical and computer engineering from the Mississippi State University, Starkville, MS, USA, in 007, 009, and 01, respectively. Subsequently, he spent one year as a Post-Doctoral Researcher with the University of California, Davis, CA, USA. He is currently with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. His research interests include statistical pattern recognition, hyperspectral image analysis, and data compression. Dr. Li is an Active Reviewer for the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, IEEE Geoscience Remote Sensing Letters, and the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING.

10 LI et al.: HYPERSPECTRAL IMAGE CLASSIFICATION BY FUSING CR AND SR 4187 Qian Du (S 98 M 00 SM 05) received the Ph.D. degree in electrical engineering from University of Maryland Baltimore County, Baltimore, MD, USA, in 000. Currently, she is a Bobby Shackouls Professor with the Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA. Her research interests include hyperspectral remote sensing image analysis and applications, pattern classification, data compression, and neural networks. Dr. Du served as a Co-Chair for the Data Fusion Technical Committee of IEEE Geoscience and Remote Sensing Society ( ), and a Chair for Remote Sensing and Mapping Technical Committee of International Association for Pattern Recognition (IAPR) ( ). She served as an Associate Editor for IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, Journal of Applied Remote Sensing, and IEEE Signal Processing Letters. Since 016, she has been the Editor-in-Chief of IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. She was the General Chair for the 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) in Shanghai, China, in 01. She is a Fellow of SPIE International Society for Optics and Photonics. She was the recipient of the 010 Best Reviewer Award from IEEE Geoscience and Remote Sensing Society. Fan Zhang (S 07 M 10) received the B.E. degree in communication engineering from the Civil Aviation University of China, Tianjin, China, the M.S. degree in signal and information processing from Beihang University, Beijing, China, and the Ph.D. degree in signal and information processing from Institute of Electronics, Chinese Academy of Science, Beijing, China, in 00, 005, and 008, respectively. He is currently an Associate Professor of electronic and information engineering with the Beijing University of Chemical Technology, Beijing, China. His research interests include synthetic aperture radar signal processing, high performance computing, and scientific visualization. Dr. Zhang is a Reviewer for the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, the IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, and the International Journal of Antennas and Propagation. Wei Hu received the B.S. and M.S. degrees in computer science from the Dalian University of Science and Technology, Dalian, China, and the Ph.D. degree in computer science from the Tsinghua University, Beijing, China, in 1999, 00, and 006, respectively. He is currently an Associate Professor of computer science with the Beijing University of Chemical Technology, Beijing, China. His research interests include computer graphics, computational photography, and scientific visualization. Dr. Hu is a Reviewer for the IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, the EuroGraphics, and the Pacific Graphics.