Band selection for target detection in hyperspectral imagery using sparse CEM

Band selection (BS) has been one of the hottest issues in the field of hyperspectral data analysis. Among the existing BS techniques, most of them are designed for classification and few of them are for target detection. In this article, we present a BS method designed specifically for target detection using sparse constrained energy minimization (CEM), named sparse constrained band selection (SCBS). SCBS is a supervised BS method embedded in CEM, which regularizes the CEM operator with a sparse constraint. Then, the sparse model is converted into a standard convex quadratic programming model where the global optimal solution can be obtained conveniently. The validation experiments show that SCBS is superior to the other compared BS methods in terms of target detection.