Road network extraction in remote sensing by a Markov object process

In this paper, we rely on the theory of marked point processes to perform an unsupervised road network extraction from optical and radar images. A road network is modeled by a Markov object process, where the objects correspond to interacting line segments. The prior model, called "quality candy" model, is constructed so as to exploit as far as possible the geometric constraints of this type of line network. Data properties are taken into account in the density of the process through a data term based on statistical tests. Optimization is realized by simulated annealing using a RJM-CMC algorithm. Some experimental results are provided on aerial and satellite images (optical and radar data).