Markov model for multispectral image analysis: application to small magellanic cloud segmentation

This paper deals with the unsupervised segmentation of astronomical multiband images. Most of these images have the particularity to be quantized on float numbers with large luminance range, on different wavelengths. These characteristics require to manipulate large amount of extremely accurate data on each spectral band, which is very different to the case of 8-bits-integer coded pixels. We present some results obtained on multiwavelength images of the small magellanic cloud, by using the marginal posterior mode (MPM) estimator on a quadtree structure under Markovian assumption. The estimation of the model parameters is then addressed with expectation-maximization (EM)-type algorithms, allowing unsupervised hyperparameter estimation. The main interest of this modeling effort lies in its generality: the algorithm handles multiwavelength floating data in a single upward and downward scan on the quadtree. A new aspect in this paper concerns the noise statistics that are supposed to be lognormal for each class. Another new aspect, is the in-scale-coding of the label map.