@inproceedings{a7c861d998404689ae3b5696f0d9fdcd,
title = "Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery",
abstract = "This paper studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy constraints that are naturally expressed within a Bayesian framework. A Gibbs sampler allows one to sample the unknown abundances and nonlinearity parameters according to the joint posterior of interest. The performance of the resulting unmixing strategy is evaluated thanks to simulations conducted on synthetic and real data.",
keywords = "hierarchical Bayesian analysis, hyperspectral images, MCMC methods, Post nonlinear mixing model",
author = "Yoann Altmann and Abderrahim Halimi and Nicolas Dobigeon and Jean-Yves Tourneret",
year = "2011",
month = jul,
day = "12",
doi = "10.1109/ICASSP.2011.5946577",
language = "English",
isbn = "9781457705380",
series = "IEEE International Conference on Acoustics, Speech and Signal Processing",
publisher = "IEEE",
pages = "1009--1012",
booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)",
address = "United States",
note = "36th IEEE International Conference on Acoustics, Speech, and Signal Processing 2011, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",
}