With increasing applications of hyperspectral imagery (HSI) in agriculture, mineralogy, military, and other fields, one of the fundamental tasks is accurate detection of the target of interest. In this article, improved sparse representation approaches using adaptive spatial support
are proposed for effective target detection in HSI. For conventional sparse representation, an HSI pixel is represented as a sparse vector whose non-zero entries correspond to the weights of the selected training atoms from a structured dictionary. For improved sparse representation, spatial
correlation and spectral similarity of adjacent neighbouring pixels are exploited as spatial support in this context. The size and shape of the spatial support is automatically determined using both adaptive window and adaptive neighbourhood strategies. Accordingly, a solution based on greedy
pursuit algorithms is also given to solve the extended optimization problem in recovering the desired sparse representation. Comprehensive experiments on three different data sets using both visual inspection and quantitative evaluation are carried out. The results from these data sets have
indicated that the proposed approaches help to generate improved results in terms of efficacy and efficiency.
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Document Type: Research Article
Affiliations:1: College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China 2: Department of Electronics and Electrical Engineering, Centre for Excellence in Signal and Image Processing, University of Strathclyde, Glasgow, G1 1XW, UK