Bayesian geometric model for line network extraction from satellite images

This paper presents a two-step algorithm to perform an unsupervised extraction of line networks from satellite images, within a stochastic geometry framework. First, we propose a new operator, providing a measure of the possibility of linear structure presence on each image pixel. Second, we propose a Bayesian model in order to extract the line network from the operator output. The prior model, a Markov object process, incorporates the topological properties of the network through interactions between objects, while the line operator answers are taken into account in the likelihood. Optimization is realized by simulated annealing using a reversible jump Monte Carlo Markov chain algorithm. An application to hydrographic network extraction is presented.