NONNEGATIVE MATRIX FACTORIZATION WITH CONSTRAINTS ON ENDMEMBER AND ABUNDANCE FOR HYPERSPECTRAL UNMIXING

Nonnegative Matrix Factorization (NMF) has been applied to hyperspectral unmixing for a few years. To relieve the non-convex problem, different constraints are imposed on NMF. But these constraints are added only on endmember or abundance. Simultaneously imposing constraints on endmember and abundance has not been tried yet. In this paper, we impose constraints on endmember and abundance at the same time in order to take a more comprehensive consideration of the properties of the hyperspectral image data. The constraints consider not only the geometric feature of endmember but also the sparsity and smoothness of abundance. The experimental performances of our method and other state-of-the-art constrained NMF methods are compared and analyzed, proving that our method is better than only imposing constraints on endmember or abundance and can improve the accuracy of hyperspectral unmixing.