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Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model

Altmann, Yoann and Dobigeon, Nicolas and Tourneret, Jean-Yves Bayesian algorithm for unsupervised unmixing of hyperspectral images using a post-nonlinear model. (2013) In: 21st European Signal and Image Processing Conference (EUSIPCO 2013), 9 September 2013 - 13 September 2013 (Marrakech, Morocco).

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This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components contaminated by an additive white Gaussian noise. The nonlinear effects are approximated by a polynomial leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding an unsupervised nonlinear unmixing algorithm. Due to the large number of parameters to be estimated, an efficient constrained Hamiltonian Monte Carlo algorithm is investigated. The performance of the unmixing strategy is finally evaluated on synthetic data.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org The original PDF of the article can be found at : http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6811647
HAL Id:hal-01239743
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université de Toulouse I-Sciences Sociales - UT1 (FRANCE)
Laboratory name:
Deposited By: IRIT IRIT
Deposited On:13 Nov 2015 13:14

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