Spatially Regularzied Sparsecem For Target Detection In Hyperspectral Images

Constrained energy minimization (CEM) is a popular method for target detection in hyperspectral images. Its variant SparseCEM uses a sparsity regularization term to promote the sparsity of the detection output. However, these approaches do not consider the spatial correlation of hyperspectral pixels, and target detection can further benefit from exploiting the spatial information. In this paper, we propose a novel constrained detection algorithm, referred to as Spatial-SparseCEM, to simultaneously force the sparsity of the output and piecewise continuity via proper regularizations. The formulated problem is solved efficiently by using alternating direction method of multipliers (ADMM). We illustrate the enhanced performance of the Spatial-SparseCEM algorithm via both synthetic and real hyperspectral data.