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12 May 2010 Sparse subspace target detection for hyperspectral imagery
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In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral images (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented by a sparse linear combination of the training samples. The sparse representation (a sparse vector representing the selected training samples) of the test sample can be recovered by solving an 0-norm minimization problem. With the recent development of Compressed Sensing theories, the minimization problem can be recast as a linear programming or solved efficiently by a greedy pursuit algorithm. Once the sparse vector is obtained, the class of the test sample can be directly determined by the behavior of the vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that HSI are usually smooth in that neighboring pixels have a similar spectral characteristic. In our proposed algorithm, a smoothness constraint is also imposed by forcing the Laplacian of the reconstructed image to be zero in the minimization process. The proposed sparsity-based algorithm is applied to several hyperspectral images to detect targets of interest. Simulation results show that our algorithm outperforms the other HSI target detection algorithms, including the popular spectral matched filters, matched subspace detectors, and adaptive subspace detectors.
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Yi Chen, Nasser M. Nasrabadi, and Trac D. Tran "Sparse subspace target detection for hyperspectral imagery", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769503 (12 May 2010);

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