A novel sparsity constrained nonnegative matrix factorization for hyperspectral unmixing

Sparsity is an intrinsic property of hyperspectral images, which means that the collected pixels can be represented by a part of materials. In this paper, a new sparsity based method for hyperspectral unmixing is proposed, referred to as the constrained sparse nonnegative matrix factorization (CSNMF). First, a novel sparse term which is explored to measure the sparsity of hyperspectral images is introduced to restrict the abundances. Second, minimum distance constraint which is convex is applied to restrict the endmembers. Then the alternating direction method of multipliers (ADMM) is used to solve the proposed CSNMF. The experimental results based on both synthetic mixtures and a real image scene demonstrate the effectiveness of the proposed approach.