Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of the accepted linear mixing model but also of...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 49; číslo 11; s. 4153 - 4162
Hlavní autori: Halimi, A., Altmann, Y., Dobigeon, N., Tourneret, J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.11.2011
Institute of Electrical and Electronics Engineers
Predmet:
Bayesian algorithm
Bayesian methods
bilinear model
Computer Science
Gibbs sampler
hyperspectral imagery
Hyperspectral imaging
Markov chain Monte Carlo (MCMC) methods
Markov processes
Networking and Internet Architecture
Signal and Image Processing
Signal to noise ratio
spectral unmixing
Gibbs sampler
Bayesian algorithm
Hyperspectral imagery
Bilinear model
Markov chain Monte Carlo (MCMC) methods
Spectral unmixing
ISSN:0196-2892, 1558-0644
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Popis
Shrnutí:Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of the accepted linear mixing model but also of a bilinear model that has been recently introduced in the literature. Appropriate priors are chosen for its parameters to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the unknown parameter vector is then derived. Unfortunately, this posterior is too complex to obtain analytical expressions of the standard Bayesian estimators. As a consequence, a Metropolis-within-Gibbs algorithm is proposed, which allows samples distributed according to this posterior to be generated and to estimate the unknown model parameters. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2010.2098414