@inproceedings{bee0cd1090d44e159252f16385685d7e,
title = "Bayesian unsupervised unmixing of hyperspectral images using a post-nonlinear model",
abstract = "This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components (referred to as endmembers) contaminated by an additive white Gaussian noise. The nonlinear effects affecting endmembers are approximated by polynomial functions leading to a polynomial post-nonlinear mixing model. A Bayesian strategy is used to estimate the parameters of this model yielding an unsupervised nonlinear unmixing algorithm. Due to the large number of parameters to be estimated, an efficient constrained HamiltonianMarkov chain Monte Carlo method is developed to sample according to the posterior of the Bayesian model. The performance of the resulting unmixing strategy is evaluated on synthetic data.",
keywords = "Hamiltonian Monte Carlo, Hyperspectral imagery, post-nonlinear model, spectral unmixing",
author = "Yoann Altmann and Nicolas Dobigeon and Jean-Yves Tourneret",
year = "2014",
month = may,
day = "8",
language = "English",
isbn = "9780992862602",
series = "European Signal Processing Conference",
publisher = "IEEE",
booktitle = "21st European Signal Processing Conference (EUSIPCO 2013)",
address = "United States",
note = "21st European Signal Processing Conference 2013, EUSIPCO 2013 ; Conference date: 09-09-2013 Through 13-09-2013",
}