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Variational methods for spectral unmixing of hyperspectral images


This paper studies a variational Bayesian unmixing algorithm for hyperspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear combination of endmembers whose corresponding fractions or abundances are estimated by a Bayesian algorithm. This approach requires to define prior distributions for the parameters of interest and the related hyperparameters. After defining appropriate priors for the abundances (uniform priors on the interval (0,1)), the joint posterior distribution of the model parameters and hyperparameters is derived. The complexity of this distribution is handled by using variational methods that allow the joint distribution of the unknown parameters and hyperparameter to be approximated. Simulation results conducted on synthetic and real data show similar performances than those obtained with a previously published unmixing algorithm based on Markov chain Monte Carlo methods, with a significantly reduced computational cost.

Dates and versions

hal-02366094 , version 1 (15-11-2019)



Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret, Hichem Snoussi. Variational methods for spectral unmixing of hyperspectral images. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, Prague, Czech Republic. pp.957-960, ⟨10.1109/ICASSP.2011.5946564⟩. ⟨hal-02366094⟩
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