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Galaxy Decomposition in Multispectral Images Using Markov Chain Monte Carlo Algorithms

  • Benjamin Perret
  • Vincent Mazet
  • Christophe Collet
  • Éric Slezak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

Astronomers still lack a multiwavelength analysis scheme for galaxy classification. In this paper we propose a way of analysing multispectral observations aiming at refining existing classifications with spectral information. We propose a global approach which consists of decomposing the galaxy into a parametric model using physically meaningful structures. Physical interpretation of the results will be straightforward even if the method is limited to regular galaxies. The proposed approach is fully automatic and performed using Markov Chain Monte Carlo (MCMC) algorithms. Evaluation on simulated and real 5-band images shows that this new method is robust and accurate.

Keywords

Bayesian inference MCMC multispectral image processing galaxy classification 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Benjamin Perret
    • 1
  • Vincent Mazet
    • 1
  • Christophe Collet
    • 1
  • Éric Slezak
    • 2
  1. 1.LSIIT (UMR CNRS-Université de Strasbourg 7005)France
  2. 2.Laboratoire Cassiopée (UMR CNRS-Observatoire de la Côte d’Azur 6202)France

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