Inverse problems arising in different synthetic aperture radar imaging systems and a general Bayesian approach for them

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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