Synthetic Aperture Radar (SAR) imaging systems are nowadays very common technics of imaging in remote
sensing and environment survey. There are different acquisition modes: spotlight, stripmap, scan; different
geometries: mono-, bi- and multi-static; and varieties of specific applications: interferometric SAR (InSAR),
polarimetric SAR etc. In this paper, first a common inverse problem framework for all of them is given, and then
basics of SAR imaging and the classical deterministic inversion methods are presented. Aiming at overcoming the
inadequacies of deterministic methods, a general probabilistic Bayesian estimation method is pioneered for solving
image reconstruction problems. In particular, two priors which simply allow the automated determination of the
hyperparameters in a Type-II likelihood framework are considered. Finally, the performances of the proposed
methods on synthetic data.
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