Unmixing hyperspectral images using the generalized bilinear model

Author

Halimi, Abderrahim ; Altmann, Yoann ; Dobigeon, Nicolas ; Tourneret, Jean-Yves

Author_Institution

IRIT/INP, Univ. of Toulouse, Toulouse, France

fYear

2011

fDate

24-29 July 2011

Firstpage

1886

Lastpage

1889

Abstract

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; geophysical image processing; geophysical techniques; gradient methods; mean square error methods; Bayesian model; Markov chain Monte Carlo method; Taylor series expansion; constrained gradient descent method; generalized bilinear model; hyperspectral image unmixing; joint posterior distribution; minimum mean square error estimator; nonlinear model; parameter estimation; positivity constraint; spectral unmixing; sum-to-one constraint; Bayesian methods; Computational modeling; Estimation; Hyperspectral imaging; Optimization; Bayesian inference; MCMC methods; bilinear model; gradient descent algorithm; hyperspectral imagery; least square algorithm; spectral unmixing;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6049492

Filename

6049492