Texture analysis through Markov random fields: urban areas extraction

We propose to analyze the texture through Markov random fields (MRF) in order to extract urban areas from high resolved satellite images. We thus define a new chain-based model derived from classical Gaussian MRF. This model which takes into account directions allows us to discriminate between oriented objects that often lead to false alarms. The texture parameters are conditional local variances estimated through a speedy heuristic method. The estimated parameters are then clustered with a new algorithm that does not require any a priori number of clusters. We ran our algorithm on optical satellite images with several ground resolution (SPOT3 and SPOT5) and on radar satellite images (ERS)