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

Bruno Figliuzzi 1 Michel Bilodeau 1 Jesus Angulo 1 Santiago Velasco-Forero 1
1 CMM - Centre de Morphologie Mathématique
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.
Keywords : linear mixing model Source separation
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01425738
Contributor : Michel Bilodeau <>
Submitted on : Tuesday, January 3, 2017 - 6:35:51 PM
Last modification on : Monday, November 12, 2018 - 10:54:19 AM

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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. Advanced Concepts for Intelligent Vision Systems: 17th International Conference, ACIVS 2016, Oct 2016, Leecy, Italy. pp.263--274, ⟨10.1007/978-3-319-48680-2_24⟩. ⟨hal-01425738⟩

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