Paper Info
Title
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery
Abstract
This paper studies a fully Bayesian algorithm for end-member extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for nonnegativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.
Year
DOI
Venue
2009
10.1109/TSP.2009.2025797
IEEE Transactions on Signal Processing
Keywords
Field
DocType
Bayes methods,image sampling,Bayesian endmember extraction,Gibbs sampler,hierarchical Bayesian model,hyperspectral imagery,linear mixing model,linear unmixing,posterior distribution,Bayesian inference,MCMC methods,endmember extraction,hyperspectral imagery,linear spectral unmixing
Endmember,Bayesian inference,Pattern recognition,Posterior probability,Hyperspectral imaging,Artificial intelligence,Prior probability,Conjugate prior,Bayes estimator,Gibbs sampling,Mathematics
Journal
Volume
Issue
ISSN
57
11
1053-587X
Citations 
PageRank 
References 
108
4.68
27
Authors
4
Search Limit
100108
Name
Order
Citations
PageRank
Nicolas Dobigeon12070108.02
Saïd Moussaoui220817.20
M. Coulon31084.68
Jean-Yves Tourneret483564.32