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Communication Dans Un Congrès Année : 2009

Subspace-based Bayesian blind source separation for hyperspectral imagery

Résumé

In this paper, a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery is introduced. Following the linear mixing model, each pixel spectrum of the hyperspectral image is decomposed as a linear combination of pure endmember spectra. The estimation of the unknown endmember spectra and the corresponding abundances is conducted in a unified manner by generating the posterior distribution of the unknown parameters under a hierarchical Bayesian model. The proposed model accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember spectra lie on a lower dimensional space. A Gibbs algorithm is proposed to generate samples distributed according to the posterior of interest. Simulation results illustrate the accuracy of the proposed joint Bayesian estimator.

Dates et versions

hal-03609224 , version 1 (15-03-2022)

Identifiants

Citer

Nicolas Dobigeon, Saïd Moussaoui, Martial Coulon, Jean-Yves Tourneret, Alfred O. Hero. Subspace-based Bayesian blind source separation for hyperspectral imagery. 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2009), Dec 2009, Aruba, Antilles, Netherlands. pp.372-375, ⟨10.1109/CAMSAP.2009.5413255⟩. ⟨hal-03609224⟩
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