A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra

Abstract : In this article, we present a Bayesian algorithm for endmember extraction and abundance estimation in situations where prior information is available for the abundances. The algorithm is considered within the framework of the linear mixing model. The novelty of this work lies in the introduction of bound parameters which allow us to introduce prior information on the abundances. The estimation of these bound parameters is performed using a simulated annealing algorithm. The algorithm is illustrated by simulations conducted on synthetic AVIRIS spectra and on the SAMSON dataset.
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Communication dans un congrès
Jacques Blanc-Talon, Cosimo Distante, Wilfried Philips, Dan Popescu, Paul Scheunders. Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Oct 2016, Leecy, Italy. Springer International Publishing, Lecture Notes in COmputer Science (0016 2016), pp.263--274, 2016, 〈10.1007/978-3-319-48680-2_24〉
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https://hal.archives-ouvertes.fr/hal-01425738
Contributeur : Michel Bilodeau <>
Soumis le : mardi 3 janvier 2017 - 18:35:51
Dernière modification le : vendredi 27 octobre 2017 - 17:36:02

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Bruno Figliuzzi, Michel Bilodeau, Jesus Angulo, Santiago Velasco-Forero. A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra. Jacques Blanc-Talon, Cosimo Distante, Wilfried Philips, Dan Popescu, Paul Scheunders. Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Oct 2016, Leecy, Italy. Springer International Publishing, Lecture Notes in COmputer Science (0016 2016), pp.263--274, 2016, 〈10.1007/978-3-319-48680-2_24〉. 〈hal-01425738〉

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