Bayesian Unmixing using Sparse Dirichlet Prior with Polynomial Post-nonlinear Mixing Model

Abstract

A sparse Dirichlet prior is proposed for estimating the abundance vector of hyperspectral images with a nonlinear mixing model. This sparse prior is led to an unmixing procedure in a semi-supervised scenario in which exact materials are unknown. The nonlinear model is a polynomial post-nonlinear mixing model that represents each hyperspectral pixel as a nonlinear function of pure spectral signatures corrupted by additive white noise. Simulation results show more than 50% improvement in the estimation error.