A novel nonlinear unmixing scheme for hyperspectral images using the nonlinear least squares technique

Hyperspectral unmixing is an important issue to analyze hyperspectral data. Based on the present mixing models, this paper proposes a new nonlinear unmixing framework for hyperspectral imagery. The proposed framework transforms the hyperspectral unmixing problem to a constrained nonlinear least squares problem by introducing the abundance nonnegative constraint, abundance sum-to-one constraint and the bound constraints of nonlinear parameters. Accordingly, an alternating iterative optimization algorithm is developed to solve the arising nonlinear least squares problem. The method decomposes the nonlinear unmixing problem into two sub-problems, which obtain alternately the abundance vectors and nonlinear parameters of the observation pixels. The experimental results on synthetic and real hyperspectral dataset demonstrate that the proposed algorithm can effectively overcome the inherent limitations of the linear mixing model. Meanwhile, the proposed algorithm performs well for noisy data, and can also be used as an effective technique for the nonlinear unmixing of hyperspectral imagery.