Hyperspectral unmixing based on nonnegative matrix factorization

Because of the local minima in the objective function, the traditional Nonnegative Matrix Factorization (NMF) algorithm is sensitive to the initial value when being applied to hyperspectral unmixing. In order to solve the problem, a new approach based on constrained NMF was proposed for decomposition of mixed pixels by introducing constraints of abundance separation and smoothness into the objective function of NMF. The algorithm can also satisfy the abundance nonnegative and sum-to-one constraints, which are necessary for hyperspectral unmixing. Experimental results on simulated and real hyperspectral data demonstrate that the proposed approach can overcome the shortcoming of local minima, and obtain better results. 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.