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By D. Mary, C. Theys, C. Aime (eds, N. Dobigeon, S. Moussaoui, M. Coulon and J. -y. Tourneret


Abstract. In this paper, we describe two fully Bayesian algorithms that have been previously proposed to unmix hyperspectral images. These algorithms relies on the widely admitted linear mixing model, i.e. each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra. First, the unmixing problem is addressed in a supervised framework, i.e., when the endmembers are perfectly known, or previously identified by an endmember extraction algorithm. In such scenario, the unmixing problem consists of estimating the mixing coefficients under positivity and additivity constraints. Then the previous algorithm is extended to handle the unsupervised unmixing problem, i.e., to estimate the endmembers and the mixing coefficients jointly. This blind source separation problem is solved in a lowerdimensional space, which effectively reduces the number of degrees of freedom of the unknown parameters. For both scenarios, appropriate distributions are assigned to the unknown parameters, that are estimated from their posterior distribution. Markov chain Monte Carlo (MCMC) algorithms are then developed to approximate the Bayesian estimators. For several decades, hyperspectral imagery has been demonstrating its high interest in numerous research works devoted to Earth monitoring. This interest can be easily explained by the high spectral resolution of the images provided by th

Year: 2014
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