Urban extraction from SAR images using local statistical characteristics and gaussian markov random field mod

As synthesis aperture radar (SAR) represents a powerful Earth observation tool for monitoring geophysical resource globally, SAR images could be used for land description and scene analysis. Actually, various applications specifically require the detection and the analysis of urban areas from original SAR images, which is a difficult task due to the speckle signal of the images and the complexity of these scenes. In this paper, an unsupervised approach to extract urban areas from SAR images has been suggested based on local statistical characteristics and texture information described by Gaussian Markov Random field (MRF) model. First, a probability map of the urban areas is computed based on local statistical characteristics, using an ffmax operator proposed by C. Gouinaud. Then the Gaussian MRF model is adopted to describe the texture of urban zones and the parameters of the model are estimated from the original image together with the probability map. Finally, the urban areas are extracted under Bayesian framework by maximum a posterior (MAP) criterion, with modeling the urban label field by Potts model. The performance of the proposed method is evaluated by experimental results on real SAR images