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

  • Bruno Figliuzzi
  • Santiago Velasco-Forero
  • Michel Bilodeau
  • Jesus Angulo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

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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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Bruno Figliuzzi
    • 1
  • Santiago Velasco-Forero
    • 1
  • Michel Bilodeau
    • 1
  • Jesus Angulo
    • 1
  1. 1.Center for Mathematical Morphology, Mines ParisTechPSL Research UniversityFontainebleauFrance

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