Squaring weighted low-rank subspace clustering for hyperspectral image band selection

Abstract

Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency matrix with accurate representation coefficients, and can adaptively determine an appropriate size for the selected band subset. The experimental results indicate that the proposed SWLRSC algorithm outperforms the state-of-the-art band selection algorithms.