HYPERSPECTRAL remote sensing imagery provides

Transcription

1 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 An Unsupervised Spectral Matching Classifier Based on Artificial DNA Computing for Hyperspectral Remote Sensing Imagery Hongzan Jiao, Yanfei Zhong, Member, IEEE, and Liangpei Zhang, Senior Member, IEEE Abstract Hyperspectral remote sensing image clustering, with the large volume, high dimensions, and temporal spatial spectral diversity, is a challenging task due to finding interesting clusters in the sparse feature space. In this paper, a novel hyperspectral clustering algorithm, namely, an unsupervised spectral matching classifier based on artificial DNA computing (UADSM), is proposed to perform the task of clustering different ground objects in specific spectral DNA feature encoding subspaces. UADSM builds up the clustering framework with the spectral encoding, optimizing, and matching mechanism by introducing the basic notions and operators of artificial DNA computing. By discretized spectral DNA feature encoding processing, the spectral shape, amplitude, and slope features of the hyperspectral data are extracted. Furthermore, the optimal clustering centers in the form of DNA strands can be found by recombining the DNA strands in the spectral DNA encoding subspace. Finally, a reasonable category for each spectral signature is automatically identified by the normalized spectral DNA similarity norm. The traditional clustering methods of k-means, ISODATA, fuzzy c-means classifier, and FCM and MoDEFC after principal component analysis transformation are provided to compare with the UADSM classifier, using Hyperspectral Digital Imagery Collection Experiment and Reflective Optics System Imaging Spectrometer hyperspectral images. The experimental results show that the UADSM classifier can achieve the best classification accuracy; hence, it is considered that the UADSM classifier is an effective clustering method for hyperspectral remote sensing imagery. Index Terms Artificial DNA computing, clustering, DNA encoding, DNA matching, DNA optimizing, hyperspectral remote sensing. I. INTRODUCTION HYPERSPECTRAL remote sensing imagery provides richer spectral information than multispectral imagery and has been used to finely classify different ground materials. Compared with supervised classification [1], unsupervised classification for hyperspectral remote sensing imagery Manuscript received November 21, 2012; revised April 12, 2013 and July 18, 2013; accepted August 12, This work was supported by the National Natural Science Foundation of China under Grant , Foundation for the Author of National Excellent Doctoral Dissertation of P.R. China (FANEDD) under Grant , and academic award for excellent Ph.D. candidates funded by the Ministry of Education of China. H. Jiao is with the School of Urban Design, Wuhan University, Wuhan , China. Y. Zhong and L. Zhang are with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan , China ( zhongyanfei@whu.edu.cn). Color versions of one or more of the figures in this paper are available online at Digital Object Identifier /TGRS can discriminate different materials without prior information. However, due to the problem of the curse of dimensionality [2] in high-dimensional spaces, all of the dimensions may not be relevant to a given cluster. Different ground materials represent different spectral absorption and reflection features on different bands, so different clusters can exist in different subspaces of the high-dimensional and sparse feature space. Therefore, hyperspectral data, with the larger volume, high dimensionality, and temporal and spatial spectral diversity, present a challenge for the traditional unsupervised clustering processing techniques such as k-means, iterative self-organizing data (ISODATA), and fuzzy c-means clustering algorithms, which originate from multispectral imagery. In recent research, some artificial intelligence methods, such as the unsupervised artificial immune classifier [3], automatic fuzzy clustering using modified differential evolution (MoDEFC) [4], and multiobjective fuzzy clustering based on differential evolution [5] and scheme with support vector machine classifier [6], have been developed to solve the multiimage clustering problem. These methods are used to estimate the class number and the distribution of clusters by multicycle iterative optimization, but when these methods are introduced to process hyperspectral data, it is difficult to reach convergence and time-consuming to search the clusters in the sparse and skewed space. To avoid the problems resulting from the high dimensionality of hyperspectral imagery, band selection and dimension reduction are conducted before hyperspectral image processing. Many feature extraction and dimension reduction techniques have been developed in hyperspectral imaging, such as principal component analysis (PCA) [7], independent component analysis [8], discrete wavelet transform [9], band reduction based on rough sets [10], projection pursuit algorithm [11], and clonal selection feature-selection algorithm [12]. After processing, the clustering is carried out on the dimension-reduced data by the traditional unsupervised classifiers. Recently, some new clustering methods have been developed to resolve the problems resulting from the high dimensionality. A divideand-conquer approach for contiguous subsets with similar characteristics has been proposed, which employs information fusion to classify the hyperspectral data [13]. A hierarchical clustering with local region-growing segmentation and global clustering has also been used to classify hyperspectral data [14]. Furthermore, a combination of clustering with feature selection based on particle swarm optimization has been IEEE

2 2 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING proposed to cluster hyperspectral data by estimating the cluster statistical parameters and by detecting the most discriminative features [15]. Although these proposed methods decrease the computational complexity for the hyperspectral clustering by different strategies, the dimension reduction destroys the continuity of the spectral signatures and the spectral absorption and reflection features, such as the spectral shape, amplitude, and slope features, which cannot then be easily used to discriminate the categories of the spectral signatures. A spectral-coding-based matching technique retains the potential discriminative information of the spectra and transforms the spectral values into a discrete set of symbols in a specific manner so that signatures can be represented and compared with each other by the new symbols more effectively [16]. Various spectral encoding and matching methods, e.g., binary coding (BC) [17], spectral-feature-based BC [18], and spectral derivative feature coding [19], have been successfully used in supervised hyperspectral data classification. In our previous work, the artificial DNA computing based spectral matching (ADSM) algorithm was proposed to perform the task of supervised classification for hyperspectral data. ADSM combines a spectral coding matching model, and the framework of artificial DNA computing, with an encoding, optimizing, and matching mechanism [20]. The spectral encoding and matching strategy based on artificial DNA computing has been demonstrated to be an efficient approach for hyperspectral classification. At the same time, it is considered that the different ground material clusters exist in different subspaces of the high-dimensional and sparse feature space [21]. Consequently, in this paper, a new clustering strategy, namely, unsupervised spectral matching based on artificial DNA computing (UADSM), is proposed to perform the task of dynamic DNA encoding subspace clustering for hyperspectral remote sensing imagery. However, the spectral-coding-based matching approaches have not been utilized to solve unsupervised classification for hyperspectral remote sensing due to the lack of prior information about the categories. In UADSM, a high-efficiency spectral encoding and matching strategy based on artificial DNA computing is introduced to cluster the hyperspectral remote sensing imagery. Based on the underlying biological properties of the DNA system and the framework of artificial DNA computing, the proposed UADSM classifier can be regarded as a self-learning, highly robust, and efficient algorithm for hyperspectral imagery clustering for the following reasons. 1) A spectral vector is transformed into the DNA strand in a manner so that a spectral signature can be represented and compared effectively and efficiently in the low-dimensional spectral feature space; consequently, the computational complexity of the high-dimensional clustering problem is greatly reduced. 2) The stability and plasticity strategies are used to capture the significant spectral DNA features on different bands for different materials and to estimate the class number of the ground objects. Furthermore, the voting processing, conducted on spectral DNA strands belonging to specific DNA clusters, is used to select different spectral DNA feature subspaces for different materials by replacing unstable or irrelevant spectral DNA codes with a 0 code. 3) A normalized spectral DNA similarity norm is proposed to calculate the similarity between the spectral DNA strands with spectral DNA clusters in different subspaces than the spectral DNA feature space. The proposed algorithm has been tested and compared to the traditional algorithms and the evolution algorithm classifiers using Hyperspectral Digital Imagery Collection Experiment (HYDICE) and Reflective Optics System Imaging Spectrometer (ROSIS) hyperspectral remote sensing images. The experimental results demonstrate that the proposed approach is highly efficient and can give a remarkable degree of classification accuracy. The remainder of this paper is organized as follows. Section II provides a synopsis of DNA computing and artificial DNA computing. Section III explains UADSM in detail. In Section IV, the experimental results are provided. Section V discusses the sensitivity with regard to the main parameters in UADSM. Finally, the conclusion is provided in Section VI. II. ARTIFICIAL DNA COMPUTING A. Original Idea of Artificial DNA Computing A DNA molecule consists of two 1-D strands, each made of alternating phosphate and deoxyribose groups, where each sugar group is covalently bound to one of four nitrogenous nucleotides, namely, guanine G, adenine A, cytosine C, and thymine T. In Adleman s pioneering work [22], DNA computing in the form of biological DNA molecules is used to solve the Hamiltonian path problem, which is a famous intractable problem in computer science. DNA computing has been used in many fields, such as the NP-complete problem [23], knapsack problem [24], traveling salesman problem (TSP) [25], [26], weighted graph problems [27], and clustering [28]. DNA computing has been successfully applied even to the signal processing field [29], [30]. The benefits of using DNA to store digital data, especially signals, include the following: information compactness, economical database replication, efficient query mechanisms, and query time that is independent of database size [29]. DNA-based matching of digital signals is used as a search or classification mechanism by quantifying the similarity between signals [31] and by carrying more plentiful genetic information than conventional evolution algorithms [32]. Artificial DNA computing is proposed based on the application of DNA computing [22], [33] and the rules of a natural genetic system. Artificial DNA computing can discriminate the tiny differences in highdimensional data using the mechanisms of DNA encoding, optimizing, and matching. It can also be used to classify or cluster the hyperspectral imagery, based on spectral signature matching. The key ideas of DNA computing follow the three points shown in Fig. 1 for the TSP: 1) encoding cities and flights into DNA strands; 2) matching the DNA strands to obtain the paths between different cities;

3 JIAO et al.: UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING 3 Fig. 1. DNA computing for the TSP. 3) recombining or selecting the DNA strands to find the solution to the TSP. According to information theory [34], heredity is relevant to information theory as an encoded communication process and is about the transmission of information and not just matter and energy. Therefore, the significant contribution of DNA computing to information science is that the information which is encoded into DNA strands can be transmitted, recombined, and processed in a biological manner. Artificial DNA computing, as a new and intelligent framework for hyperdimensional data classifying and mining, is inspired by Adleman s original experiment and the Darwinian evolution theory. By exploring and simulating a biological genetic system, artificial DNA computing can quickly and robustly solve problems with high-volume and high-dimensional data. In our previous work, artificial DNA computing was used to resolve spectral recognition [20], [35], successfully combining a spectral encoding and matching model. B. Model of Artificial DNA Computing for High-Dimensional Clustering Due to the lack of prior information about the highdimensional clustering and the temporal and spatial spectral diversity, the class number estimation, the clustering center location, and the subspace selection are considered as important and challenging problems for high-dimensional clustering. As presented in Fig. 2, when high-dimensional clustering is transformed from the feature space to the DNA encoding space of the spectral feature, the recombining and matching mechanism for the DNA strands is used to explore the optimal DNA clustering centers and to discriminate the categories of the spectral signatures. In order to better understand artificial DNA computing for hyperdimensional data clustering, some illustrations of the basic notions and operators are given as follows. Fig. 2. Relationship between spectral clustering and artificial DNA computing. 1) DNA Strand: The DNA strand of artificial DNA computing, represented as DNA, is composed of a discrete code, represented as code {G, A, C, T }. 2) Length of the DNA Code and the DNA Encoding Strands: The length of the DNA code is calculated by { 1, if code {G, A, C, T } code = (1) 0, else. The length of the DNA strands encoded by G, A, C, and T is calculated by N DNA = code n. (2) n=1 3) Boolean and Operation: The Boolean AND operator is conducted on the codewords on the corresponding position of two or multi-dna strands. The situation of the Boolean AND operator as with two DNA codewords by { codei, if code code i code j = i = code j (3) 0, else. The situation for the Boolean AND operator for M DNA codewords is code i,i=1,...m.

4 4 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING First, the numbers of different codewords for M DNA codewords (Num code ) are calculated, respectively, by M Num code = code i code, code=g, A, C or T. (4) i=1 The Boolean AND operator for M DNA codewords as is calculated by { M arg max{num code }, if (max(num code )/M > λ) code i = code i=1 0, else. (5) where λ is the threshold for the operator with T DNA codewords and the value of λ is empirically set as 0.5. If max(num code )/M > λ, half of the DNA codewords code i,i=1,...m are consistent. It is worth mentioning that in (3) and (5), the 0 code is introduced when the codewords on the corresponding position of two or multi-dna strands are inconsistent. 4) Similarity Measuring for DNA Codes: During the processing of the pattern recognition for DNA strands, the similarity between two DNA strands is evaluated by DNA similarity code i code j DNA or DNA real similarity code i code j real. code i code j DNA is denoted by { 1, if codei = code j code i code j DNA = 0, else. (6) code i code j real is denoted by code i code j real 1, if code i = code j = 1 V (code i) V (code j ) 4.0, if code i code 0 0, else 4, if code i = G 3, if code where V (code i )= i = A.Itisusedtotransform from DNA code to real for calculating the DNA real 2, if code i = C 1, if code i = T similarity. 5) Similarity Measuring for DNA Strands: The similarity between DNA strands DNA i and DNA j is evaluated by DNA similarity DNA i DNA j DNA or DNA real similarity DNA i DNA j real. DNA i DNA j DNA is denoted by DNA i DNA j DNA = (7) N code n i code n j DNA. (8) n=0 DNA i DNA j real is denoted by DNA i DNA j real = N code n i code n j real. (9) n=0 Fig. 3. DNA encoding processing. (a) Spectral shape feature encoding. (b) Spectral amplitude feature encoding. (c) Spectral slope feature encoding. III. UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING In this paper, the UADSM classifier for hyperspectral remote sensing imagery clustering is proposed with a DNA encoding, recombining, and matching mechanism. In UADSM, we consider a hyperspectral image to be composed of d bands and N pixels. Each pixel is represented by a vector X i R d = [x 1 i,x2 i,...xd i ], i=1, 2,...,N.Thebth band image is denoted by X b R d =[x b 11,...,xij b,...xb N l N s ], b=1, 2,...,d, where N l is the number of lines and N s is the number of samples. A. DNA Encoding for Spectra In UADSM, DNA encoding is used to extract discriminative spectral absorption and reflection features for the hyperspectral data. To describe the spectral absorption and reflection features, the spectral shape, amplitude, and slope features of the DNA encoding approach are introduced. By the DNA encoding processing, the features of the continuous spectra are discretized as a DNA strand (Fig. 3). 1) DNA Encoding for the Spectral Shape Feature: Spectral shape feature DNA encoding is conducted on the original spectrum. First, each original pixel spectrum of the hyperspectral

5 JIAO et al.: UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING 5 cube X i R d =[x 1 i,x2 i,...xd i ], i =1, 2,...,N, is sorted according to increasing value by bands, and the result is i,...xd i ], i =1, 2,...,N, and xmin i = x 1 i,. The three thresholds of DNA encoding for the spectral shape feature are, respectively, determined as follows: X i Rd =[x 1 i,x2 x max i = x d i T l = x ((3 d)/4) i, T o = x (d/2) i, and T r = x (d/4) i. For the jth band, the DNA codeword for the spectral shape feature DNA shape i,j of spectral signature X i can be determined by DNA shape i,j = G if x j i [T l,x max i ] A if x j i [T o,t l ] C if x j i [T,j=1, 2,...,d. (10) r,t o ] T if x j i [ ] x min i,t r 2) DNA Encoding for the Spectral Amplitude Feature: Spectral amplitude feature DNA encoding is used to extract the overall radiation characteristics of different pixel spectra by the thresholds determined on each band image. The thresholds of spectral amplitude feature DNA encoding for the ith line and jth sample on the bth band are defined as follows. First, the bth band image X b R d =[x b 11,...,x b ij,...xb N l N s ] is sorted according to increasing value, the result being X b R d = [x b 11,...,x b ij,...xb N l N s ]. The three DNA encoding thresholds of the bth band image for the spectral amplitude feature are then, respectively, determined as follows: Tm b = x b (3 (N l N s ))/4, Tp b = x b (N l N s )/2, and T n b = x b (N l N s )/4. The three magnitude feature spectra, Tm, b Tp b, Tn, b are composed by combining the thresholds of each band. Finally, for the bth band, the DNA codeword for the spectral amplitude feature DNA magnitude i,b of spectral signature X i can be determined by G if x b i [ ] Tm,x b b max DNA magnitude A if x b i i,b = [ ] Tp b,tm b C if x b i [ ] Tn,T b p b,j=1, 2,...,d. T if x b i [ ] x b min,tb n (11) 3) DNA Encoding for the Spectral Slope Feature: The spectral DNA gradient feature is extracted on the derivative spectrum. By acquiring the absolute value of the difference between two adjacent bands, the derivative spectrum is obtained by (12). The sorting processing is conducted on the derivative spectrum according to increasing value, the sequence being sorted as Y i =[y i,1,y i,2,...,y i,d 1 ], and the thresholds are determined as follows: T h = y i,(3 (d 1))/4, T q = y i,(d 1)/2, and T k = y i,(d 1)/4 Y i R d =[y i,1,y i,2,...,y i,d 1 ], where y i,j = x i,j x i,j+1. (12) Finally, the spectral DNA gradient feature is determined by DNA gradient i,j G if y i,j [T h,y i,max ] A if y = i,j [T q,t h ],j=1, 2,...,d 1. (13) C if y i,j [T k,t q ] T if y i,j [y i,min,t k ] After DNA encoding processing, the pixel vector x i of the hyperspectral cube is encoded as the DNA feature strand DNA i, as presented in Fig. 4. The DNA feature strand is composed of 3 d 1 DNA codewords on the spectral DNA feature space, and the DNA feature cube is composed of DNA feature strands with the same structure as the hyperspectral cube. B. Recombination for DNA Clustering Centers After the DNA encoding procedure, the hyperspectral remote sensing data are converted to discrete spectral DNA feature strands. Unsupervised classification for hyperspectral imagery can therefore be resolved by automatically exploring and identifying the reasonable patterns contained in the spectral DNA cube. Because temporal and spatial spectral diversity results in instability of the spectral DNA feature codewords, the same cluster can have different DNA strands. It is difficult to cluster hyperspectral data with the direct spectral encoding and matching methods. It is noticed that the same category ground objects represent the consistency of the key and discriminative spectral features on the specific bands. Therefore, if we can find the appropriate DNA clustering centers (DCs) and the DNA strands tight around the clustering center, the key and discriminative spectral DNA features for each category can be determined and can be used to efficiently cluster the hyperspectral data. In this paper, a recombination mechanism inspired by adaptive resonance theory for binary sequence pattern recognition [36] is used to find the optimal DNA clustering centers in the DNA encoding space. It is composed of similarity and heterogeneity tests for creating DNA clustering centers (DNA DCs ) and finding the most similar unclassified DNA feature strands (DNA Fs ) around DNA DCs, with voting processing for updating the codewords of DNA DCs. The procedure of the DNA classifier is presented in Fig. 5, and the detailed implementation is provided as follows: Step 1. Clustering Initialization: The unsupervised DNA algorithm begins with all the DNA Fs and empty DNA DCs.The maximum number of DNA DCs for the UADSM classifier is settled before the clustering. The DNA Fs from hyperspectral imagery are input into the UADSM classifier separately. If the DCs are empty, the initial DNA Fs is the same as the new cluster center DNA 1 DCs. Step 2. Similarity and Heterogeneity Tests: The similarity test is used to evaluate how near the DNA Fs is to the DNA DCs and can find the DNA Fs in accordance with DNA DCs. The heterogeneity test is used to create a new DNA DCs when the number of DNA DCs does not reach the maximum class number. a) Similarity test: The similarity test calculates how near the DNA feature strand DNA Fs is to DNA DCs. Therefore, if (14) is satisfied, then DNA Fs can be regarded as sufficiently close to the ith DNA clustering center DNA i DCs in the DNA encoding space, and the category of DNA Fs will be assigned to DNA i DCs ( ) DNA i DCs DNA DNA Fs DNA i >β. (14) DCs max i

6 6 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Fig. 4. Transformation from a hyperspectral cube to a DNA feature cube. (a) Structure of a hyperspectral image. (b) DNA feature encoding. Fig. 5. Procedure of the unsupervised DNA classifier. Fig. 7. DNA codeword updating for DCs. Fig. 6. Diagram of the similarity and heterogeneity tests. (a) Similarity and heterogeneity tests for the initial DCs. (b) Similarity and heterogeneity tests for all of the feature strands. The function max i ( ) represents obtaining the maximum value when i takes different values. The closest DNA i DCs to DNA Fs is denoted as DNA max DCs. b) Heterogeneity test: If the similarity test is not satisfied, the heterogeneity test will be carried out. When the number of DNA DCs does not reach the maximum class number of DNA DCs and the heterogeneity test for DNA max DCs is satisfied by (15), the new DNA DCs will be created according to DNA Fs DNA max DCs DNA Fs <ρ. (15) DNA Fs If DNA Fs is used to create new DNA DCs,thisDNA Fs will be labeled, and it will not be required to conduct the heterogeneity test for creating new DNA DCs. When all of the DNA strands are input into tests, the algorithm switches to step 3. A diagram of the similarity and heterogeneity tests in the global DNA encoding space is shown in Fig. 6. In Fig. 6(a), the similarity is conducted on initial DCs, the DNA feature strands tight around the initial DCs are labeled, and the heterogeneity

7 JIAO et al.: UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING 7 Fig. 8. Clustering DNA strands in different DNA encoding subspaces. test is used to determinate new DCs. In Fig. 6(b), the similarity test determines all of the DNA feature strands that are close to the two DCs and all of the DNA feature strands that do not satisfy the heterogeneity test. Therefore, no new DCs are created. Step 3. Voting Processing for the Discriminative Spectral DNA Features: In order to find the key and discriminative spectral DNA feature codes on the specific position, the DNA Fs that are close to original DNA DCs are used to vote for the new DCs in the DNA encoding subspace by (4) and (5). The codewords on the ith position of DNA Fs that are close to DNA DCs can be represented as code i,i=1,...n.theith codeword will be replaced by n i=1 code i. A diagram of the voting processing is shown in Fig. 7. The input strands belonging to DCs are used to vote for updating the DCs. Bands 1, 3, 4, n 3, n 2, and n are regarded as the stable spectral DNA features, so the spectral DNA feature is retained in the updated DCs. In addition, bands 2 and n 1 are regarded as unstable feature bands, and as a consequence, the spectral DNA feature is replaced with a 0 code. The parameter γ is set as 0.5 in (5). Due the 0 code, the different DNA DCs will be mapped into different DNA encoding subspaces after the different DNA DCs are updated by the voting processing. All of the DNA Fs can be clustered into different classes by comparing the normalized DNA similarity measure in the different DNA encoding subspaces, based on a maximum similarity rule. The normalized DNA similarity measure is calculated by (16). The processing is presented in Fig. 8. Step 4. Deleting Processing for DNA DCs : If the heterogeneity test is not satisfied among the DNA clustering centers DNA new DCs, the deleting processing for DNA DCs will be carried out for the most similar DNA DCs pair, and the DNA DCs with 0 codes will be deleted. The heterogeneity test for the DNA DCs is calculated by max i DNA i DCs DNAj DCs real ) <ρ, i j. min ( DNA idcs, DNAjDCs (16) If DNA new DCs is the same as DNA DCs, the looping of the recombining processing will terminate; otherwise, the algorithm Fig. 9. HYDICE image. (a) HYDICE image. (b) Validation field. will switch to step 2, and DNA new DCs is considered as the old DNA clustering center in the next looping process. C. Clustering the Hyperspectral Imagery by the Matching Mechanism The final DNA DCs are combined by the different spectral DNA features for each category. The final DNA DCs obtained by the DNA system are used to classify the spectral DNA cube by the DNA similarity measure in (16), and the unsupervised classification result by the DNA system is obtained. IV. EXPERIMENTS AND ANALYSIS The proposed UADSM is implemented and tested using two different types of hyperspectral remote sensing images, HYDICE and ROSIS images, to test the performance of the different unsupervised classifiers. The previously mentioned unsupervised classifiers, k-means and ISODATA, and the unsupervised classifiers of fuzzy c-means and automatic fuzzy clustering using modified differential evolution (MoDEFC) [4] combined with PCA feature extraction algorithms (named PCA+FCM and PCA+MoDEFC) are also carried out and compared with the proposed algorithm. A. Experiment 1 HYDICE Imagery The first experimental hyperspectral remote sensing image is urban data captured by the HYDICE sensor in October The image area is located at Copperas Cove, near Fort Hood, TX, USA, with an image size of and spectral and spatial resolutions of 10 nm and 2 m, respectively. There are

8 8 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Fig. 10. Results with the HYDICE urban data. (a) k-means. (b) ISODATA. (c) Fuzzy c-means. (d) PCA+fuzzy c-means. (e) PCA+MoDEFC. (f) UADSM. 210 bands in the data, and by assigning the 63rd, 52nd, and 36th bands as the R, G, and B components, respectively, a false-color 3-D cube can be obtained, as shown in Fig. 9(a). As we can see, ground objects in the area include a highway near the top of the image, a shopping mall along the highway, a parking lot in front of the mall, some roads, grass, trees, and rows of houses. In addition, due to the low solar altitude, trees and houses cast long shadows on the ground. Considering the materials of the ground objects, there are both asphalt roads and concrete roads, and the asphalt roads show strong absorption, while the concrete roads have a higher intensity of radiation absorption. The tree, grass, and soil classes present differences on the near-infrared bands. To evaluate the classification accuracy, a test field map is provided in Fig. 9(b), based on the ground truth obtained by field sampling. The classes of ground objects are the following: roof, tree, concrete road, shadow, grass, asphalt road, and soil. Fig. 10 illustrates the classification results using the k-means, ISODATA, fuzzy c-means, PCA+fuzzy c-means, PCA+MoDEFC, and proposed UADSM classifiers. The class number is determined in different ways. The number of classes for the ISODATA classifier ranges from 6 to 10, and the upper limit of the class number is set as 10 in the k-means, fuzzy c- means, PCA+fuzzy c-means, and PCA+MoDEFC classifiers. The PCA feature reduction is conducted, and the first five features are used for the classification. The classification accuracies are calculated after the class-merging operation. The key parameters of UADSM are set as follows: β =0.95, ρ =0.6, and the maximum number of the UADSM classifier is set as 10. The clustering result is automatically obtained by the deleting operator for the DNA clustering centers. The visual comparisons of the six unsupervised classifications in Fig. 10 show varying degrees of pixel assignment accuracy. The grass, bare soil, and tree categories are well separated by fuzzy c-means, PCA+MoDEFC, and UADSM, but the roof class gets a poor classification result, and the shadow class is exaggerated in the fuzzy c-means classifier. Although the asphalt and concrete roads are well classified by the k-means, ISODATA, and PCA+fuzzy c-means classifiers, the grass class is replaced by the tree class in the k-means and ISODATA classifiers, and the classification result mixes up asphalt road and shadow in the PCA+fuzzy c-means classifier because of the PCA feature reduction. The proposed classification method is robust with regard to the diversity of the spectra because it exploits the shape, magnitude, and gradient information of the spectra. The classes of the UADSM result are properly separated by the optimization processing of the recombination strategy for DNA strand identification. Table I lists the results of the comparisons between the ground truth data and the classified images obtained by the six classifiers, using overall accuracy and the kappa coefficient, based on the confusion matrix shown in Table II. From Table I, it is apparent that the UADSM classifier produces better classification results than the other classifiers, with the best overall accuracy and kappa coefficient of 83.31% and , respectively. It can be seen from the confusion matrix of UADSM in Table II that roof and concrete road are well separated, with accuracies of 99.55% and 97.96%, and some pixels in the soil class are separated into the grass class, with the lowest class accuracy of 64.75%, because of mixed pixels between the soil and grass classes. As a whole, from the overall accuracy and kappa coefficient, UADSM shows a good classification ability for the HYDICE data. The time consumed by the unsupervised classifiers is compared in Table I. The PCA+FCM classifier takes the shortest time to cluster the data after PCA feature extraction, but because MoDEFC takes too much time to estimate the class number and to optimize the cluster centers, PCA+MoDEFC is inefficient. Due to the spectral DNA feature encoding, the high-dimensional clustering is transformed into discrete feature code clustering in a lowdimensional space. It takes 2 18 to cluster the hyperspectral

9 JIAO et al.: UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING 9 TABLE I COMPARISON OF THE SIX CLUSTERING METHODS TABLE II CONFUSION MATRIX OF THE SIX CLUSTERING METHODS imagery with UADSM, and the encoding processing costs 28, while the optimizing and matching processing costs 1 50.The times of the comparison algorithms do not contain the merging processing for similar clusters. The spectral DNA strands of the final DCs are represented in Fig. 11, which is used to classify the HYDICE data into the seven classes shown in Fig. 10(f). It is clear that the ground objects of different materials are separated by different spectral DNA features on the different bands. Taking the tree class for example: 1) the spectral shape DNA features are retained on the near-infrared bands with the DNA codewords of G, so it is illustrated that the high reflectance on the near-infrared bands is the significant feature of the tree class; 2) the spectral amplitude DNA features are retained with the DNA codewords of T; therefore, the band values of the tree class are lower than the other classes on each band image; and 3) the spectral slope DNA features are characterized by the DNA codewords of T, so it is considered that the spectral signature of the tree class changes smoothly between adjacent bands. Therefore, it can be concluded that the significant features of the spectral signatures can be captured by the artificial DNA computing, and reasonable categories for the ground objects can be decided by the stability and plasticity strategy in the HYDICE experiment. Because the spectral features in the form of values can be converted into DNA information, the similarity between the unclassified pixels and DCs can be calculated by (16), based on the normalization similarity measuring criterion, even in the different DNA feature encoding subspaces.

10 10 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Fig. 11. Spectral DNA strands of the final DCs. B. Experiment 2 ROSIS Imagery The second hyperspectral image data set was acquired by the airborne ROSIS sensor at the urban test area of Pavia, northern Italy. The whole data set size is pixels, and we use a subset of in our study. This data set was provided by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society [37]. According to the specifications, the number of bands of the ROSIS-03 sensor is 115, with a spectral coverage ranging from 0.43 to 0.86 μm. Thirteen bands are removed due to noise, and the remaining 102 bands with a spatial resolution of 1.3 m per pixel are processed in the experiment. Fig. 12(a) shows the information about the ROSIS data set, with a pseudocolor image of bands 60, 27, and 17. The ground truth of the ROSIS data set is provided in Fig. 12(b). In the clustering processing, seven classes of interest are considered, namely, asphalt road, tree, water, building, shadow, grass, and bare soil. Six clustering algorithms are compared with the ROSIS data set: k-means, ISODATA, fuzzy c-means, PCA+fuzzy c-means, PCA+MoDEFC, and the proposed UADSM. The maximum class number of k-means, fuzzy c-means, PCA+fuzzy c-means, and PCA+MoDEFC is determined as 10, respectively, while the class number range of ISODATA is from 6 to 10. The classifi- Fig. 12. ROSIS data set. (a) Pseudocolor image (bands 60, 27, and 17). (b) Ground truth. cation accuracies of the clustering methods are calculated after the class-merging operation. The key parameters of UADSM are set as follows: β =0.95, ρ =0.6, the maximum number of clusters for the UADSM classifier is set as 10, and the final result with the class number as 7 is obtained by the automatic cluster-merging operation. Visual comparisons of the results are shown in Fig. 13. Due to water and shadow with low reflectance in the whole band range, k-means, ISODATA, and fuzzy c-means, with the minimum distance measuring criterion, cannot discriminate water

11 JIAO et al.: UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING 11 Fig. 13. Results with the urban ROSIS data set. (a) k-means. (b) ISODATA. (c) Fuzzy c-means. (d) PCA+fuzzy c-means. (e) PCA+MoDEFC. (f) UADSM. TABLE III COMPARISON OF THE SIX CLASSIFICATION METHODS USING OVERALL ACCURACY AND THE KAPPA COEFFICIENT and shadow. In addition, these three classifiers confuse building and bare soil because the band values of some buildings are close to that of bare soil. Because the discriminative features for similar categories could be lost during the PCA transform processing, the tree and grass classes are confused by the PCA+FCM classifier, and the shadow and water classes can only be partly discriminated. Even the category of shadow is considered as the asphalt road with low reflectance in the PCA+MoDEFC classifier. The UADSM classifier acquires a good classification effect between shadow and water, and building with bare soil, in spite of some uncertain pixels between the bare soil and building classes. The classification accuracies and kappa coefficients of the six classifiers are listed in Table III. The best OA and kappa coefficient are acquired by UADSM, with 90.19% and , respectively. The fuzzy k-means classifier acquires the worst classification accuracy of 68.86% and a kappa coefficient of because of the incorrect classification between the tree and grass, building and bare soil, and shadow and water classes. Due to the misclassification of the shadow class, the kappa coefficient still shows a bigger difference than the overall accuracy in the k-means, ISODATA, fuzzy c-means, and PCA+MoDEFC classifiers. The confusion matrix of the six classifiers is provided in Table IV. In addition, a computational efficiency comparison of the six classifiers is provided in Table III. Because the dimensions of the ROSIS data are lower than the HYDICE data, the computation time of UADSM for the ROSIS data is less than that for the HYDICE data. The time cost of encoding processing is 19, and the recombining and matching processing cost is V. S ENSITIVITY ANALYSIS The adjustable parameters of the UADSM algorithm include the DNA encoding parameters, the threshold λ in the operator, and the similarity and heterogeneity measuring thresholds β and ρ. Due to the lack of prior information about the ground truth, it is difficult to optimize these thresholds of the DNA encoding. As a consequence, in the experimental part (Section IV), equivalence partitioning (1/4, 2/4, and 3/4) is utilized and is a relatively reliable method for the DNA encoding, considering the computational complexity of the encoding parameter optimization processing. Furthermore, the operator for the M DNA codewords is defined by (5). This operator is used to update the clustering centers by the voting processing. When the threshold λ issetas0.5, halfofthe M DNA codewords are consistent. Therefore, in consideration of the majority rule, it can be reasonably considered that the parameter λ is set as 0.5. To evaluate the sensitivity of the adjustable parameters to the UADSM algorithm, experiments with different parameter combinations are carried out as follows. A. DNA Encoding Parameters The partitioning thresholds of the DNA encoding of the UADSM algorithm are used to discretize the continuous spectral data. Different threshold combinations can partition

12 12 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING TABLE IV CONFUSION MATRIX OF THE SIX CLASSIFICATION METHODS the hyperspectral data into different DNA encoding cubes. In the sensitivity analysis experiment, different parameter combinations (1/3, 1/2, and 2/3), (1/4, 1/2, and 3/4), and (2/5, 1/2, and 3/5) are introduced (Table V). It is clear that the classification results of the different partitioning threshold combinations are all satisfactory. Due to the optimizing and matching processing of the UADSM algorithm, reasonable clustering results can be obtained with different partitioning thresholds. B. Threshold λ in the Operator The threshold λ in the operator is used to evaluate the consistency of the DNA codewords. Usually, in consideration of the majority rule, it can be reasonably considered that the parameter λ is set as 0.5. When the threshold λ is less than 0.5, the DNA strand of the clustering center will have fewer 0 codes; when λ is greater than 0.5, the unstable encoding positions of the DNA strand will be determined as 0 codes, and the available encoding positions will decrease. More 0 codes mean less discriminative features; however, the appropriate subspaces for the different classes will be difficult to find with less 0 codes. It is illustrated in Fig. 14 that, when the parameter λ is set as 0.5, the most reasonable clustering result is acquired. C. Similarity and Heterogeneity Measuring Thresholds β and ρ UADSM has two key parameters when the similarity and heterogeneity tests are conducted: the similarity measuring threshold β and the heterogeneity measuring threshold ρ. The threshold β is used to find the most similar unclassified strand set which presents encoding consistency on the spectral absorption and reflection feature bands. Furthermore, the discriminative and key encoding positions can be determined by voting processing, as shown in Fig. 6. The higher the threshold β is, the less DNA strands belong to the set that is consistent with the specific DNA clustering center. The threshold ρ is used to determine whether a new DNA clustering center will be created when the unclassified strand is not similar to the existing DNA clustering centers. A lower heterogeneity threshold ρ results in more divergence between the existing DCs and the newly created DCs; hence, the categories will be separated loosely.

13 JIAO et al.: UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING 13 TABLE V SENSITIVITY ANALYSIS OF THE DNA ENCODING THRESHOLDS When the parameter ρ =0.6, 0.5, or 0.4, all of the classes can be discriminated by UADSM. When ρ =0.3, similar encoding cluster centers cannot be created by the heterogeneity test; therefore, the shadow cannot be separated (Table VII). It is concluded that, when β is set from 0.8 to 0.95 and ρ is set from 0.4 to 0.6, the final clustering results are stable. Fig. 14. Sensitivity analysis of threshold λ. When a high value is assigned to ρ, it is possible that more similar DCs will be created and more detailed categories will be separated. The different thresholds β and ρ will cause different numbers of clusters in the resulting image. Because the clustering number of the clustering results is not the same as the class number of the validation data, the classification accuracies are difficult to evaluate. Therefore, the cluster number is provided during the sensitivity analysis, and the cluster results mandatorily cluster into the given cluster number when the deleting processing for the DNA clustering centers is carried out. Experiments with different parameters are conducted on the ROSIS data set to assess the sensitivity of the thresholds β and ρ by visual comparison and overall accuracy and kappa coefficient. The threshold β is greater than ρ, and the sensitivity analysis for the threshold β is performed when the threshold ρ is set as 0.6 and the cluster number is set as 7. When β =0.95 or β =0.9, the class shadow can be well separated; when β =0.8, the DNA encoding similarity of the shadow and water causes some class confusion; and when β =0.7, the shadow cannot be separated from water, and the grass and tree are difficult to discriminate. The different parameters cause different subclass combinations in the final result, but the subclass combinations have clear physical meanings (Table VI). The experiments for the sensitivity analysis of the heterogeneity measuring threshold ρ are conducted with β =0.95, and the cluster number is set as 7. VI. CONCLUSION In this paper, artificial DNA computing has been employed for the clustering of hyperspectral remote sensing data, in keeping with artificial DNA computing based supervised spectral encoding and matching classification (ADSM) [20]. A novel spectral clustering algorithm, namely, unsupervised hyperspectral data classification by artificial DNA computing based spectral matching (UADSM), has been proposed. UADSM is conducted according to the framework of the spectral encoding, optimizing, and matching mechanism which is constructed by ADSM. A modified spectral DNA encoding method with spectral shape, magnitude, and gradient feature extraction is introduced. Furthermore, a 0 code is used to replace unstable codewords during the spectral encoding. The reasonable patterns in the spectral DNA cube for the hyperspectral remote sensing data are automatically explored and identified by the recombination processing. When the samples are input into the UADSM classifier, the new DNA encoding patterns can be created as a new DNA cluster by UADSM, and the old patterns can be retained and acclimatized to the new surroundings. During the iterative process, the robust and reasonable spectral DNA clusters can be found when the spectral DNA clusters have extracted all of the key spectral features of all of the input samples. The classification result is determined by comparing the spectral DNA strands in the spectral DNA cube with the spectral DNA clusters under the normalized spectral DNA similarity measurement. Compared with the traditional clustering methods, the UADSM classifier achieves the best overall accuracy and kappa coefficient. In addition, it has been demonstrated that the artificial DNA computing has a higher efficiency and a lower computing cost. It can therefore be considered that the UADSM classifier is a feasible unsupervised classifier for hyperspectral remote sensing data. The parameters in the UADSM classifier are discussed, and sensitivity analyses are conducted with regard to the adjustable

14 14 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING TABLE VI SENSITIVITY ANALYSIS OF THE THRESHOLDβ(ρ =0.6) TABLE VII SENSITIVITY ANALYSIS OF THE HETEROGENEITY MEASURING THRESHOLDρ parameters of the UADSM algorithm, including the DNA encoding parameters, the threshold λ in the operator, and the similarity and heterogeneity measuring thresholds β and ρ. Although the influence of the parameters is recognized, adaptive parameter selection will be introduced into the UADSM classifier in our future work. ACKNOWLEDGMENT The authors would like to thank the Editor, Associate Editor, and anonymous reviewers for their helpful comments and suggestions that improved this paper and Prof. P. Gamba from the University of Pavia, Pavia, Italy, for providing the ROSIS data set. REFERENCES [1] A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, Recent advances in techniques for hyperspectral image processing, Remote Sens. Environ., vol. 113, no. S1, pp. S110 S122, Sep [2] J.R.Jensen,Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed. Englewood Cliffs, NJ, USA: Prentice-Hall, [3] C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer-Verlag, [4] Y. Zhong, L. Zhang, B. Huang, and P. Li, An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery, IEEE Trans. Geosci. Remote Sens., vol. 44, no. 2, pp , Feb [5] U. Maulik and I. Saha, Automatic fuzzy clustering using modified differential evolution for image classification, IEEE Trans. Geosci. Remote Sens., vol. 48, no. 9, pp , Sep [6] Y. Zhong, S. Zhang, and L. Zhang, Automatic fuzzy clustering based on adaptive multi-objective differential evolution for remote sensing imagery, IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 6, no. 5, pp , Oct [7] A. Mukhopadhyay and U. Maulik, Unsupervised pixel classification in satellite imagery using multiobjective fuzzy clustering combined with SVM classifier, IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp , Apr [8] M. D. Farrell, Jr. and R. M. Mersereau, On the impact of PCA dimension reduction for hyperspectral detection of difficult targets, IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp , Apr [9] J. Wang and C. I. Chang, Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis, IEEE Trans. Geosci. Remote Sens., vol. 44, no. 6, pp , Jun [10] S. Kaewpijit, J. Le Moigne, and T. El-Ghazawi, Automatic reduction of hyperspectral imagery using wavelet spectral analysis, IEEE Trans. Geosci. Remote Sens., vol. 41, no. 4, pp , Apr [11] S. Hong, S. Yi, and L. Zhiyan, Hyperspectral bands reduction based on rough sets and fuzzy c-means clustering, in Proc. 20th IEEE IMTC, 2003, vol. 2, pp [12] L. O. Jimenez and D. A. Landgrebe, Hyperspectral data analysis and supervised feature reduction via projection pursuit, IEEE Trans. Geosci. Remote Sens., vol. 37, no. 6, pp , Nov [13] L. Zhang, Y. Zhong, B. Huang, J. Gong, and P. Li, Dimensionality reduction based on clonal selection for hyperspectral imagery, IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp , Dec [14] Y. Q. Zhao, L. Zhang, and S. G. Kong, Band-subset-based clustering and fusion for hyperspectral imagery classification, IEEE Trans. Geosci. Remote Sens., vol. 49, no. 2, pp , Feb [15] L. Sanghoon and M. M. Crawford, Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure, IEEE Trans. Image Process., vol.14,no.3,pp ,Mar [16] A. Paoli, F. Melgani, and E. Pasolli, Clustering of hyperspectral images based on multiobjective particle swarm optimization, IEEE Trans. Geosci. Remote Sens., vol. 47, no. 12, pp , Dec [17] C. I. Chang, J. Wang, C. C. Chang, and C. Lin, Progressive coding for hyperspectral signature characterization, Opt. Eng., vol. 45, no. 9, pp , Sep [18] X. Jia and J. Richards, Binary coding of imaging spectrometer data for fast spectral matching and classification, Remote Sens. Environ., vol. 43, no. 1, pp , Jan [19] S. Qian, A. B. Hollinger, D. Williams, and D. Manak, Fast threedimensional data compression of hyperspectral imagery using vector quantization with spectral-feature-based binary coding, Opt. Eng., vol. 35, no. 11, pp , Nov [20] C. I. Chang, S. Chakravarty, H. M. Chen, and Y. C. Ouyang, Spectral derivative feature coding for hyperspectral signature analysis, Pattern Recogn., vol. 42, no. 3, pp , Mar [21] H. Jiao, Y. Zhong, and L. Zhang, Artificial DNA computing-based spectral encoding and matching algorithm for hyperspectral remote sensing

15 JIAO et al.: UNSUPERVISED SPECTRAL MATCHING CLASSIFIER BASED ON ARTIFICIAL DNA COMPUTING 15 data, IEEE Trans. Geosci. Remote Sens., vol. 50, no. 10, pp , Oct [22] H. P. Kriegel, P. Kröger, and A. Zimek, Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, correlation clustering, ACM Trans. Knowl. Discov. Data,vol.3,no.1,pp.1 58, Mar [23] L. M. Adleman, Molecular computation of solutions to combinatorial problems, Science, vol. 266, no. 5187, pp , Nov [24] Q. H. Liu, L. M. Wang, A. G. Frutos, A. Condon, R. Corn, and L. Smith, DNA computing on surfaces, Nature, vol. 403, no. 6766, pp , Jan [25] M. Darehmiraki and H. M. Nehi, Molecular solution to the 0 1 knapsack problem based on DNA computing, Appl. Math. Comput.,vol. 187,no.2, pp , Apr [26] S. Y. Shin, I. H. Lee, D. Kim, and B. Zhang, Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing, IEEE Trans. Evol. Comput., vol. 9, no. 2, pp , Apr [27] J. Y. Lee, S.-Y. Shin, T. H. Park, and B. Zhang, Solving traveling salesman problems with DNA molecules encoding numerical values, Biosystems, vol. 78, no. 1 3, pp , Dec [28] Z. Ibrahim, Y. Tsuboi, and O. Ono, Hybridization-ligation versus parallel overlap assembly: An experimental comparison of initial pool generation for direct-proportional length-based DNA computing, IEEE Trans. NanoBiosci., vol. 5, no. 2, pp , Jun [29] R. B. A. Bakar, J. Watada, and W. Pedrycz, DNA approach to solve clustering problem based on a mutual order, Biosystems, vol. 91, no. 1, pp. 1 12, Jan [30] S. A. Tsaftaris, A. K. Katsaggelos, T. N. Pappas, and E. T. Papoutsakis, How can DNA computing be applied to digital signal processing? IEEE Signal Process. Mag., vol. 21, no. 6, pp , Nov [31] S. A. Tsaftaris, A. K. Katsaggelos, T. N. Pappas, and E. T. Papoutsakis, DNA computing from a signal processing viewpoint, IEEE Signal Process. Mag., vol. 21, no. 5, pp , Sep [32] S. Tsaftaris, A. K. Katsaggelos, T. N. Pappas, and E. T. Papoutsakis, DNA-based matching of digital signals, in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2004, vol. 5, pp [33] O. H. Kwon, K. Y. Wang, and J. Y. Kim, DNA inspired digital signal pattern matching algorithm, in Proc. Frontiers Converg. Biosci. Inf. Technol., 2007, pp [34] R. Lipton, DNA solution of hard computational problems, Science, vol. 268, no. 5210, pp , Apr [35] G. Battail, Heredity as an encoded communication process, IEEE Trans. Inf. Theory, vol. 56, no. 2, pp , Feb [36] H. Jiao, Y. Zhong, L. Zhang, and P. Li, Unsupervised remote sensing image classification using an artificial DNA computing, in Proc. ICNC11, Shanghai, China, 2011, pp [37] G. A. Carpenter and S. Grossberg, Adaptive resonance theory, in The Handbook of Brain Theory and Neural Networks, M.A.Arbib,Ed. Cambridge, MA, USA: MIT Press, 2003, pp [38] G. Licciardi, F. Pacifici, D. Tuia, S. Prasad, T. West, F. Giacco, C. Thiel, J. Inglada, E. Christophe, J. Chanussot, and P. Gamba, Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest, IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp , Nov Yanfei Zhong (M 11) received the B.S. degree in information engineering and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2002 and 2007, respectively. He has been with the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, since 2007, where he is currently a Professor. He has published more than 50 research papers, including 20 peerreviewed articles in international journals such as IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING and IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B. His research interests include multi- and hyperspectral remote sensing image processing, artificial intelligence, and pattern recognition. Dr. Zhong was the recipient of the National Excellent Doctoral Dissertation Award of China in 2009 and New Century Excellent Talents in University of China in He is a Guest Editor of a special issue on remote sensing image processing in the Soft Computing journal (Springer). He was a Referee of the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,andPattern Recognition. Liangpei Zhang (M 06 SM 08) received the B.S. degree in physics from Hunan Normal University, ChangSha, China, in 1982, the M.S. degree in optics from Xi an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi an, China, in 1988, and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in He is currently the Head of the Remote Sensing Division, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. He is also a Chang-Jiang Scholar Chair Professor appointed by the Ministry of Education, China. He is currently the Principal Scientist of the China State Key Basic Research Project ( ), appointed by the Ministry of National Science and Technology of China to lead the remote sensing program in China. He is an Executive Member (Board of Governor) of the China National Committee of International Geosphere Biosphere Programme. He also serves as an Associate Editor of the International Journal of Ambient Computing and Intelligence, International Journal of Image and Graphics, International Journal of Digital Multimedia Broadcasting, Journal of Geo-spatial Information Science, andjournal of Remote Sensing. Hehas more than 260 research papers and is the holder of five patents. His research interests include hyperspectral remote sensing, high-resolution remote sensing, image processing, and artificial intelligence. Dr. Zhang is a Fellow of the Institution of Electrical Engineers, an Executive Member of the China Society of Image and Graphics, and others. He regularly serves as a Cochair of a series of SPIE Conferences on Multispectral Image Processing and Pattern Recognition, Conference on Asia Remote Sensing, and many other conferences. He edits several conference proceedings, issues, and geoinformatics symposiums. He is currently an Associate Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. evolution algorithm. Hongzan Jiao received the B.S. degree in land resource management and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 2008 and 2013, respectively. He is currently a Full-Time Lecturer with the School of Urban Design, Wuhan University. His research interests include multi- and hyperspectral data analysis, pattern recognition in remote sensing images, and artificial intelligence including artificial DNA computing, genetic algorithm, and differential