A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra
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.
KeywordsBayesian Approach Prior Information Simulated Annealing Algorithm Bayesian Algorithm Gibbs Sampling Algorithm
This work has been supported by the European Commission project M3S (Molecular Signature Detection with Multi-modal Microscopy Scanner) under the ICT PSP Call (Contract no. 621152). The authors would like to thank MEDyC team from Reims University for providing Raman spectra used in the experimental part. Special thanks also go to Jacques Klossa (TRIBVN) for discussion.
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