Parameter Selection Criteria for TomoSAR Focusing

The synthetic aperture radar (SAR) tomography (TomoSAR) inverse problem is commonly tackled in the context of the direc-tion-of-arrival estimation theory. The latter allows achieving super-resolution, along with ambiguity levels reduction, thanks to the use of parametric focusing methods, as multiple signal classification (MUSIC), and statistical regularization techniques, like the maximum-likelihood inspired adaptive robust iterative approach (MARIA). Nevertheless, in order to correctly suit the considered signal model, MUSIC and most regularization ap-proaches require an appropriate setting of the involved parame-ters. In both cases, the accuracy of the retrieved solutions de-pends on the right selection of the assigned values. Thus, with the aim of dealing with such an issue, this article addresses sev-eral parameter selection strategies, adapted specifically to the TomoSAR scenario. Parametric techniques as MUSIC solve the TomoSAR problem in a different manner as the regularization methods do, hence, each approach demands different methodol-ogies for the proper estimation of their parameters. Conse-quently, we refer to the Kullback-Leibler information criterion for the model order selection of parametric techniques as MUSIC, whereas we rather explore the Morozov’s discrepancy principle, the L-Curve, the Stein’s unbiased risk estimate and the generalized cross-validation, to choose the regularization pa-rameters. After the incorporation of these criteria to MUSIC and MARIA, respectively, their capabilities are first analyzed through simulations, and later on, utilizing real data acquired from an urban area