Galaxy Decomposition in Multispectral Images Using Markov Chain Monte Carlo Algorithms

Benjamin Perret (1) , Mazet Vincent (1) , Christophe Collet (1) , Eric Slezak (2)

1 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection
2 OCA - Observatoire de la Côte d'Azur

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.

Mots clés: Bayesian inference; MCMC; multispectral image processing; galaxy classification

Domaines: Traitement des images [eess.IV]

Benjamin Perret, Mazet Vincent, Christophe Collet, Eric Slezak. Galaxy Decomposition in Multispectral Images Using Markov Chain Monte Carlo Algorithms. Scandinavian Conference on Image Analysis, Jun 2009, Oslo, Norway. pp.209-218, ⟨10.1007/978-3-642-02230-2_22⟩. ⟨hal-00749593⟩