Region‐division‐based joint sparse representation classification for hyperspectral images. Issue 10 (10th July 2019)

• Record Type: Journal Article
• Authors: Yan, Jingwen; Chen, Hongda; Zhai, Yikui; Liu, Yinan; Liu, Lei
• Is Part Of: IET image processing. Volume 13:Issue 10(2019)
• Page Start: 1694
• Page End: 1704
• Publication Date: 2019-07-10
• Subjects: image segmentation -- image classification -- geophysical image processing -- feature extraction -- hyperspectral imaging -- image representation -- Markov processes; JSRC -- heterogeneous pixels -- test pixel -- classification performance -- original HSI -- regional information -- HSI pixels -- multiple classification results -- classification methods -- HSI classification -- hyperspectral images -- region‐division‐based joint sparse representation classification method -- heterogeneous region problem -- hyperspectral image classification -- RDJSRC method -- hidden Markov random field; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6667
• Languages: English
• ISSNs: 1751-9659

Abstract

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.