OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

Bayesian separation of spectral sources under non-negativity and full additivity constraints

Dobigeon, Nicolas and Moussaoui, Saïd and Tourneret, Jean-Yves and Carteret, Cédric Bayesian separation of spectral sources under non-negativity and full additivity constraints. (2009) Signal Processing, vol. 8 (n° 12). pp. 2657-2669. ISSN 0165-1684

[img]
Preview
(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
674kB

Official URL: http://dx.doi.org/10.1016/j.sigpro.2009.05.005

Abstract

This paper studied Bayesian algorithms for separating linear mixtures of spectral sources under non-negativity and full additivity constraints. These two constraints are required in some applications such as hyperspectral imaging and spectroscopy to get meaningful solutions. A hierarchical Bayesian model was defined based on priors ensuring the fulfillment of the constraints. Estimation of the sources as well as the mixing coefficients was then performed by using samples distributed according to the joint posterior distribution of the unknown model parameters. A Gibbs sampler strategy was proposed to generate samples distributed according to the posterior of interest. The generated samples were then used to estimate the unknown parameters. The performance of the algorithm was first illustrated by means of simulations conducted on synthetic signals. The application to the separation of chemical mixtures resulting from Raman spectroscopy was finally investigated. The proposed Bayesian algorithm provided very promising results for this application. Particularly, when the computational times is not a study constraint, the proposed method clearly outperforms other standard NMF techniques, which can give approximative solutions faster. Perspectives include the development of a similar methodology for unmixing hyperspectral images. Some results were already obtained for the unmixing of known sources. However, the joint estimation of the mixing coefficients (abundances) and the sources (endmembers) is a still an open and challenging problem.

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com The original PDF of the article can be found at Signal Processing website : http://www.sciencedirect.com/science/journal/01651684
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS
Other partners > Ecole Centrale de Nantes (FRANCE)
Other partners > Ecole des Mines de Nantes (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
Université de Toulouse > Université de Toulouse I-Sciences Sociales - UT1
Other partners > Université de Nantes (FRANCE)
Other partners > Université Henri Poincaré-Nancy1 - UHP (FRANCE)
Other partners > University of Michigan - U-M (USA)
Laboratory name:
Statistics:download
Deposited By: Nicolas DOBIGEON

Repository Staff Only: item control page