Phase characterization of polarimetric SAR images

High Resolution (HR) Synthetic Aperture Radar (SAR) Single Look Complex (SLC) observations, mainly of strong scattering scenes or objects show phase patterns. Phase patterns may occur due to the system behavior or they may be signatures of the imaged objects. Since state of the art stochastic models of SAR SLC data describe mainly the pixel information. Now studies are needed to elaborate better models for the full information content. Thus, new statistical models of HR SAR SLC are proposed, they aim at the characterization of the spatial phase feature of Polarimetric SAR (PolSAR) SLC data, i.e. they describe multi-band, complex valued textures. The definition of texture must be changed because it is not anymore characterizing the optical features but the electromagnetic properties of the illuminated targets. The content of the SAR image is a stochastic process characterized from its own structure and geometry, which differs from the real one of the illuminated scene, and is dominated from strong scatterers. Nevertheless we are going to accept the classical texture definition, inherited from computer vision, in homogeneous areas and, furthermore, we are going to extend it for a characterization of isolated and structured objects The proposed models are in the class of simultaneous Auto-Regressive (sAR) defined on a generalized set of cliques in the pixel vicinity. Models may have different orders, thus capturing different degrees of the data complexity. To cope with the problem of estimation and model order selection Bayesian inference is used. The results are presented on PolSAR data.