@INPROCEEDINGS{15.whispers.variability,
   author =  "Abderrahim Halimi and Nicolas Dobigeon and Jean-Yves Tourneret and Paul Honeine",
   title =  "Hyperspectral unmixing accounting for spatial correlations and endmember variability",
   booktitle =  "Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)",
   address =  "Tokyo, Japan",
   year  =  "2015",
   month =  "2 - 5~" # jun,
   acronym =  "WHISPERS",
   url_link= "https://ieeexplore.ieee.org/document/8075442",
   url_paper  =  "http://honeine.fr/paul/publi/15.whispers.variability.pdf",
   abstract={This paper presents an unsupervised Bayesian algorithm for hyper-spectral image unmixing accounting for endmember variability. This variability is obtained by assuming that each pixel is a linear combination of random endmembers weighted by their corresponding abundances. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed model is unsupervised since it estimates the abundances and both the mean and the covariance matrix of each endmember. A classification map indicating the class of each pixel is also obtained based on the estimated abundances. Simulations conducted on a real dataset show the potential of the proposed model in terms of unmixing performance for the analysis of hyperspectral images.}, 
   keywords={Bayesian inference, hyperspectral, Bayes methods, covariance matrices, geophysical image processing, hyperspectral imaging, image classification, normal compositional model, spatial correlations, endmember variability, unsupervised Bayesian algorithm, random endmembers, additive noise, hyper-spectral image unmixing, mean matrix, covariance matrix, classification map, Bayes methods, Hyperspectral imaging, Correlation, Estimation, Markov processes, Standards, Covariance matrices, Hyperspectral imagery, endmember variability, image classification, Markov chain Monte-Carlo}, 
   doi={10.1109/WHISPERS.2015.8075442}, 
   ISSN={2158-6276}, 
}

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