We present a Bayesian segmentation algorithm which is part of a fully Bayesian approach for automatic information extraction from satellite images. It was shown that pyramidal image models based on multi-scale Markov random fields in combination with a texture model yield good classification and segmentation results. The texture model is used for an initial characterization and then an optimal segmentation is inferred using the multi-scale random field defined on a pyramid structure. Segment probabilities are calculated in a fine-to-rough analysis and segmentation is performed by a rough-to-fine decision algorithm that maximizes the a posteriori probability of the pyramid. The procedure is iterated until it converges to a stable solution.
We improve the maximization procedure by optimizing the underlying pyramidal structure of the multi-scale Markov random field. Neighborhood dependencies are switched on and off according to the image data. The hierarchical organization allows a fast computation and the segmentations obtained are smooth, even at coarse scales. Additionally it has the advantage to be exactly tractable.