Building detection by Markov object processes

This work aims at detecting buildings in digital aerial photographs. We model a set of buildings by a configuration of objects. We define a point process on the set of configurations, which could be divided into two parts: the first one is a prior model on the configurations which uses interactions between objects. The second one is a data model which enforces the coherence with the images. Thus we obtain a distribution /spl pi/ which has to be maximized. In order to achieve this maximum, we use a Monte Carlo Markov Chain simulation-a Metropolis-Hastings-Green algorithm-mixed with simulated annealing. Then we test this method on both synthetic and real data.