Target detection is one of the most important applications in hyperspectral remote sensing image analysis. Sparse representation method has been considered to be effective in hyperspectral target detection. In this method, a sparse representation with respect to a certain pixel in hyperspectral imagery means a linear combination of few data vectors in the data dictionary. An training dictionary consisting of both target and background samples in the same feature space is first constructed and test pixels are sparsely represented by decomposing over the dictionary. Though sparse representation is considered to preserve main information of most pixels, inevitable indeterminacy may lead to different representations of same or similar pixels. In this paper, a manifold regularized sparsity model is proposed to deal with this problem. A graph regularization term is incorporated into the sparsity model under the manifold assumption that similar data pixels should have similar sparse representation. Then a modified simultaneous version of the SP algorithm (SSP) is implemented to obtain the recovered sparse vectors which are composed of sparse coefficients corresponding to both target sub-dictionary and background sub-dictionary. Once the sparse vectors are obtained, the residual between original test samples and estimate recovered from target sub-dictionary as well as the residual between original test samples and estimate recovered from background sub-dictionary are calculated to determine the test pixels’ class. The proposed algorithm is applied to real hyperspectral image to detect targets of interest. Experimental results show a more accurate target detection performance with this proposed model over that with conventional sparse models.

Date Published: 22 May 2014
PDF: 9 pages
Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 912418 (22 May 2014); doi: 10.1117/12.2057583

Published in SPIE Proceedings Vol. 9124:
Satellite Data Compression, Communications, and Processing X
Bormin Huang; Chein-I Chang; José Fco. López, Editor(s)