Region‐division‐based joint sparse representation classification for hyperspectral images

Abstract

In this article, a region-division-based joint sparse representation classification (RDJSRC) method is proposed to solve the heterogeneous region problem in the joint sparse representation classification (JSRC) method used in hyperspectral image (HSI) classification. The RDJSRC method incorporates regional information, obtained by the hidden Markov random field (HMRF), into the JSRC to reduce the interference of heterogeneous pixels in the neighbourhood of the test pixel and finally improve the classification performance. The framework of this method is as follows. The first several principal components (PCs) are initially selected to be the new HSI by transforming the original HSI with the PC analysis algorithm. Then, the regional information containing the spatial structure of the HSI is obtained by applying the HMRF algorithm to the first PC. Through incorporating this regional information into the JSRC procedure, the initial label of the test pixel can be jointly determined by the new HSI pixels within the homogeneity in the search window. Ultimately, the final label of the test pixel is determined by a voting strategy based on multiple classification results. Compared with several classification methods, experimental results, indicate that this method achieves improvement from 2 to 3% in HSI classification.