DocumentCode :
2058888
Title :
Bayesian unsupervised unmixing of hyperspectral images using a post-nonlinear model
Author :
Altmann, Yoann ; Dobigeon, Nicolas ; Tourneret, Jean-Yves
Author_Institution :
ENSEEIHT/IRIT/TeSA, Univ. of Toulouse, Toulouse, France
fYear :
2013
fDate :
9-13 Sept. 2013
Firstpage :
1
Lastpage :
5
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 Hamiltonian Markov 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 :
AWGN; Markov processes; Monte Carlo methods; hyperspectral imaging; image processing; parameter estimation; polynomials; Bayesian unsupervised unmixing; Hamiltonian Markov chain Monte Carlo method; additive white Gaussian noise; hyperspectral image unmixing; hyperspectral images; nonlinear effects; parameter estimation; polynomial functions; polynomial post-nonlinear mixing model; Abstracts; Bayes methods; Indexes; Hamiltonian Monte Carlo; Hyperspectral imagery; post-nonlinear model; spectral unmixing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
Conference_Location :
Marrakech
Type :
conf
Filename :
6811647
Link To Document :
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