A novel approach for hyperspectral unmixing based on Nonnegative Matrix Factorization

Traditional Nonnegative Matrix Factorization (NMF) algorithm is sensitive to the initial value when being applied to hyperspectral unmixing, because of the local minima in the objective function. In order to solve the problem, two constraints of abundance separation and smoothness are introduced into the NMF algorithm. The proposed algorithm retains the advantages of NMF, and effectively overcomes the shortcoming of local minima at the same time. Experimental results on simulated and real hyperspectral data demonstrate that the proposed approach can overcome the shortcoming of local minima, and obtain better results with respect to other state-of-art approaches. Meanwhile, the algorithm performs well for noisy data, and can also be used for the unmixing of hyperspectral data in which pure pixels do not exist.