A discriminated similarity matrix construction based on sparse subspace clustering algorithm for hyperspectral imagery. (January 2019)

• Record Type: Journal Article
• Authors: Yan, Qing; Ding, Yun; Zhang, Jing-Jing; Xia, Yi; Zheng, Chun-Hou
• Is Part Of: Cognitive systems research. Volume 53(2019)
• Page Start: 98
• Page End: 110
• Publication Date: 2019-01
• Subjects: Hyperspectral imagery -- Sparse subspace clustering -- Similarity matrix -- Sparse representation vector; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.01.003
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: The clustering of hyperspectral images is a challenging task because of the high dimensionality of the data. Sparse subspace clustering (SSC) algorithm is one of the popularly used clustering algorithm for high dimensionality data. However, SSC has not fully used the spectral and spatial information during similarity matrix construction based on single sparse representation coefficient for hyperspectral Imagery (HSI) clustering. In this paper, two novel similarity matrix construction methods named as Cosine-Euclidean similarity matrix (abbreviated as CE) and Cosine-Euclidean dynamic weighting similarity matrix (abbreviated as CEDW) are proposed for HSI clustering. They can combine the high spectral information and rich spatial information. Firstly, CE utilizes the cosine similarity of spectral information based on overall sparse representation vectors and classical Euclidean distance of spatial information to construct a novel similarity matrix. Secondly, inheriting CE merits, dynamic weighting adjustment method is introduced to CEDW for some external influence factors to the HSI information. Several experiments on HSI demonstrated that the proposed algorithms are effective for HSI clustering.