Abstract
We consider the problem of hyper-parameter selection in advanced image reconstruction algorithms used in synthetic aperture radar (SAR) imaging. To deal with the parameter selection problem in these algorithms, we propose the use of unbiased predictive risk estimation and generalized cross-validation techniques. We demonstrate the effectiveness of the applied methods through experiments based on electromagnetically simulated realistic data.
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