Cluster constraint based sparse NMF for hyperspectral imagery unmixing

Nonnegative matrix factorization (NMF) has been applied to hyperspectral unmixing in recent years. Different constraints based on geometrical or statistical properties of end-member and abundance are incorporated into NMF model to improve unmixing result. In this paper, a new regularizer based on spectral cluster information is proposed to strengthen the constrained relationship between original image and abundance maps. The new algorithm makes abundances of similar pixels close and abundances of dissimilar pixels be separated completely. Additionally, L_1/2 sparsity constraint is adopted to make the solutions sparse. Comparative results on real and synthetic hyperspectral datasets prove our proposed method could improve the hyperspectral unmixing accuracy.