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… (More)
DOI: 10.1109/IGARSS.2012.6351277


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