Hierarchical multispectral galaxy decomposition using a MCMC algorithm with multiple temperature simulated annealing

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

We present a new method for the parametric decomposition of barred spiral galaxies in multispectral observations. The observation is modelled with a realistic image formation model and the galaxy is composed of physically significant parametric structures. The model also includes a parametric filtering to remove non desirable aspects of the observation. Both the model and the filter parameters are estimated by a robust Monte Carlo Markov chain (MCMC) algorithm. The algorithm is based on a Gibbs sampler combined with a novel strategy of simulated annealing in which several temperatures allow to manage efficiently the simulation effort. Besides, the overall decomposition is performed following an original framework: a hierarchy of models from a coarse model to the finest one is defined. At each step of the hierarchy the estimate of a coarse model is used to initialize the estimation of the finer model. This leads to an unsupervised decomposition scheme with a reduced computation time. We have validated the method on simulated and real 5-band images: the results showed the accuracy and the robustness of the proposed approach.

Mots clés: Modelling and recovery of physical attributes; Monte Carlo Markov chain algorithms; Simulated annealing; Hierarchical decomposition; Inverse problems; Astronomy

Domaines: Traitement des images [eess.IV]

Benjamin Perret, Mazet Vincent, Christophe Collet, Eric Slezak. Hierarchical multispectral galaxy decomposition using a MCMC algorithm with multiple temperature simulated annealing. Pattern Recognition, 2011, 44 (6), pp.1328-1342. ⟨10.1016/j.patcog.2010.11.021⟩. ⟨hal-00749588⟩