HYPERSPECTRAL UNMIXING ALGORITHM BASED ON NONNEGATIVE MATRIX FACTORIZATION

Nonnegative Matrix Factorization (NMF) factorizes a nonnegative matrix into product of two positive matrixes, which is widely used in hyperspectral unmixing. However, the convergence speed of NMF is comparatively slower, and a large number of local minimum will be existed when it is directly adopted in the factorization of hyperspectral image mixed pixels. A modified hyperspectral unmixing method based on NMF is presented in this paper. The local linear embedding (LLE) algorithm is used to reduce the dimension of hyperspectral data. The sparseness and smoothness constraints are added into the cost function. The NeNMF algorithm is used in updating endmember matrix and abundance matrix for hyperspectral data. The results show that this method can achieve good result of classification.