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 leads to an unmixing procedure in a semisupervised scenario in which exact materials are unknown. The nonlinear model is a polynomial post‐nonlinear mixing model, which represents each hyperspectral pixel as a nonlinear function of pure spectral signatures corrupted by additive white noise. Simulation results show more than 50% reduction in the estimation error. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.