Abstract
Over the last few years, an enormous amount of studies has been devoted to astronomical image restoration. The new challenge, nowadays, has been reported on the use of multichannel techniques to restore, segment and classify such images. The multichannel image segmentation problem, is a new field of great interest for the astronomical community. This paper presents an unsupervised method to automatically segment multispectral astronomical images, which is the first stage toward the automatic classification of astronomical objects. The proposed method relies on a hierarchical Markovian modeling on a quadtree, including the estimation of all involved parameters. The parameters of the prior model are automatically calibrated while the estimation of the noise parameters is solved by identifying generalized distribution mixtures, using an iterative conditional estimation (ICE) procedure. Generalized Gaussian (GG) distributions are considered to model intensity distribution of the multispectral images.
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