Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

作     者：Tang, Yidong Huang, Shucai Xue, Aijun 

作者机构：Air Force Engn Univ Sch Air & Missile Def Xian 710051 Peoples R China 

出 版 物：《MATHEMATICAL PROBLEMS IN ENGINEERING》 (Math. Probl. Eng.)

年 卷 期：2016年第2016卷第1期

页      面：1-10页

核心收录：

学科分类：08[工学] 0701[理学-数学] 

基　　金：National Natural Science Foundation of China Aeronautical Science Foundation of China 

主　　题：SPARSE approximations BINARY system (Mathematics) HYPERSPECTRAL imaging systems IMAGE processing SEARCH algorithms 

摘      要：The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.