Bayesian Texture Extraction from Metric Resolution SAR Images

摘要

The recognition and classification of urban structures from SAR observations is a particularly complex task. In this article we present a new concept aiming at the accurate and detailed classification of the city scenes observed with metric resolution SAR sensors. SAR images of build-up areas at resolution of 2-3 meters are characterized by strong patterns induced by the geometry of buildings and the phenomenology of scattering of the radar signals. Thus, resulting in high complexity images. The accuracy of image interpretation relies on the descriptive power of the low level image information extraction. The article presents a method based on the Bayesian concepts. A hierachical 3 layers model is used for the SAR observations. The first layer describes the speckle effect as a Gamma distribution, the second, the cross-section, is modeled as Gibbs Random Field (GRF), the third layer the parameters of the Gibbs random field is considered a Jeffrey's prior. The GRF describes the cross-section structures induced by the geometry of the buildings. The model is non-stationary, its parameters adapt locally to the image structures.