Unsupervised segmentation of textured satellite and aerial images with Bayesian methods

We investigate Bayesian solutions to unsupervised image segmentation based on the double Markov random field model. Inference on the number of classes in the image is done with reversible jump Metropolis moves. These moves are implemented by splitting and merging classes. Tests are conducted on satellite and aerial images.