A New Feature Selection Method for Hyperspectral Image Classification Based on Simulated Annealing Genetic Algorithm and Choquet Fuzzy Integral

Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 537268, 13 pages http://dx.doi.org/10.1155/2013/537268 Research Article A New Feature Selection Method for Hyperspectral Image Classification Based on Simulated Annealing Genetic Algorithm and Choquet Fuzzy Integral Hongmin Gao, Lizhong Xu, Chenming Li, Aiye Shi, Fengchen Huang, and Zhenli Ma College of Computer and Information Engineering, Hohai University, Nanjing 211100, China Correspondence should be addressed to Lizhong Xu; Received 1 June 2013; Revised 14 September 2013; Accepted 15 September 2013 Academic Editor: Gianluca Ranzi Copyright © 2013 Hongmin Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hyperspectral remote sensing technology is a rapidly developing new integrated technology that is widely used in numerous areas. Rich spectral information from hyperspectral images can aid in the classification and recognition of the ground objects. However, the high dimensions of hyperspectral images cause redundancy in information. Hence, the high dimensions of hyperspectral data must be reduced. This paper proposes a hybrid feature selection strategy based on the simulated annealing genetic algorithm (SAGA) and the Choquet fuzzy integral (CFI). The band selection method is proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then, the selecting bands are further refined by CFI. Experimental results show that the proposed method can achieve higher classification accuracy than traditional methods. 1. Introduction Hyperspectral remote sensors peculiarly provide measurements of the Earth’s surface with very high spectral resolution, usually resulting in tens of channels. Unlike multispectral sensors, the high spectral resolution renders hyperspectral remote sensors very powerful in applications requiring the identification of subtle differences in ground covers (e.g., material quantification and target detection). On the other hand, the large-dimensional data spaces generated by these sensors introduce challenging methodological problems. In the context of supervised classification, the most important methodological issue raised by these sensors is the so-called curse of dimensionality (also known as the Hughes effect) that occurs when the numbers of features and of available training samples are unbalanced [1]. Meanwhile, hyperspectral remote sensing images have nonlinear properties. These nonlinear properties originate from the multiscattering between photons and ground targets, within pixel spectral mixing, and from scene heterogeneity. In addition, given that the pixel size in most remote sensing systems is sufficiently large to include different types of land cover, classification error arises and produces unreliable classification results. In this case, traditional classifiers may fail completely. In remote sensing literature, numerous methods have been developed to solve the hyperspectral data classification problem. A successful approach to hyperspectral data classification is based on the support vector machine (SVM). SVM determines two classes by identifying the optimal separating hyperplane that maximizes the margin between the closest training sample and the separating hyperplane. Data samples located at the hyperplane border are referred to as support vectors and are used to create a decision surface. The properties of SVM for both full-dimensional and reduced-dimensional data have been investigated, while multi-class SVM strategies have been considered in [2]. Hyperspectral image classification using different kernelbased approaches has been analyzed and compared, and SVM has been found to be more useful than other kernelbased methods in [3]. SVM classification performance is compared with other well-known neural approaches in [4], which exhibited that SVM provides simplicity, robustness, and increased classification accuracy compared with neural 2 networks. In addition, some improved SVM methods have also been successfully used in hyperspectral image classification. The proposed method, called contextual SVM using Hilbert space embedding showed significant improvement over other methods on several hyperspectral images in [5]. A semisupervised method for addressing a domain adaptation problem based on multiple-kernel SVMs in the classification of hyperspectral data was presented in [6]. Thus, SVM is very suitable for hyperspectral image classification. However, dimension reduction is not sufficiently considered in SVM. Commonly used dimension reduction methods fall into two categories, namely, feature selection and feature extraction. Since every band of hyperspectral data has its own corresponding image, the feature extraction approach maps a high-dimensional feature space to low-dimensional space via linear or nonlinear transformation. However, the original physical interpretation of the image cannot be retained. Thus, feature extraction approaches are unsuitable for the dimension reduction of hyperspectral images. Given that the spectral distance between adjacent bands in the hyperspectral data is only 10 nm and because the correlation between them is extremely high [7], a considerable redundancy is observed, which should be largely reduced by the feature selection or band selection methods to improve classification efficiency and accuracy. A semisupervised feature-selection technique for hyperspectral image classification was developed in [8]. A method for unsupervised band selection by transforming the hyperspectral data into complex networks was presented in [9]. Therefore, a new dimension reduction method is proposed that combines the simulated annealing genetic algorithm (SAGA) with the Choquet fuzzy integral (CFI). A population and temperature ladder-based new genetic algorithm (GA) or the so-called SAGA was recently proposed to examine a sample from a distribution defined on a space of finite binary sequence. The feature selection strategy of hyperspectral images based on GA and SVM was proposed in [10, 11]. A GA-based feature selection and local-Fisher’s discriminant analysis-based feature projection are performed for effective dimensionality reduction in [12]. But SAGA method works by simulating a parallel population of samples with different temperatures. The population is updated via selection, mutation, cross-over, and exchange operations that are highly similar with GA. SAGA has the learning capability of GA, as well as the fast-mixing capability of parallel tempering (simulated tempering). In most cases, classification accuracy is only used as the fitness function, but internal relations between bands and classes (...truncated)