Generalized bilinear model based nonlinear unmixing using semi-nonnegative matrix factorization
Nonlinear spectral mixing models have recently been receiving attention in hyperspectral image processing. This work presents a novel optimization method for nonlinear unmixing based on a generalized bilinear model (GBM), which considers second-order scattering effects. Semi-nonnegative matrix factorization is used for optimization to process a whole image in a matrix form. The proposed method is applied to an airborne hyperspectral image with many endmembers and shows good performance both in unmixing quality and computational cost with simple implementation. The effect of endmember extraction on nonlinear unmixing is investigated and the impact of the nonlinearity on abundance maps is demonstrated.