DMCA
BAYESIAN ALGORITHM FOR UNSUPERVISED UNMIXING OF HYPERSPECTRAL IMAGES USING A POST-NONLINEAR MODEL
| Citations: | 1 - 1 self |
BibTeX
@MISC{Altmann_bayesianalgorithm,
author = {Yoann Altmann and Nicolas Dobigeon and Jean-yves Tourneret},
title = {BAYESIAN ALGORITHM FOR UNSUPERVISED UNMIXING OF HYPERSPECTRAL IMAGES USING A POST-NONLINEAR MODEL},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
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 contaminated by an additive white Gaussian noise. The nonlinear effects are approximated by a polynomial leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding an unsupervised nonlinear unmixing algorithm. Due to the large number of parameters to be estimated, an efficient constrained Hamiltonian Monte Carlo algorithm is investigated. The performance of the unmixing strategy is finally evaluated on synthetic data. Index Terms — Hyperspectral imagery, unsupervised spectral unmixing, Hamiltonian Monte Carlo, post-nonlinear model.
Keyphrases
synthetic data unsupervised nonlinear unmixing algorithm unmixing strategy hyperspectral image unmixing unknown pure spectral component polynomial post-nonlinear mixing model nonlinear effect bayesian algorithm index term hyperspectral imagery post-nonlinear model pixel reflectance large number nonlinear mixing model unsupervised spectral unmixing hamiltonian monte carlo algorithm post-nonlinear function additive white gaussian noise polynomial leading hamiltonian monte carlo
