We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It
has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield
superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data
collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some
hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence
there is interest in developing methods for automatic parameter choice. We investigate Stein's unbiased risk estimator
(SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar
imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) "Backhoe Data Dome,"
to demonstrate and discuss the effectiveness of these methods.