Enhanced-resolution SAR tomography using the weighted covariance fitting criterion

Abstract

The use of maximum likelihood (ML)-inspired statistical regularization approaches to solve the synthetic aperture radar (SAR) tomography (TomoSAR) nonlinear ill-conditioned inverse problem, has been successfully demonstrated in previous related studies. Within the main advantages of these approaches there is the retrieval of resolution-enhanced tomograms using a reduced number of passes, performing also suppression of artifacts and reduction of the ambiguity levels. Nonetheless, such techniques are constrained to the probability density function (pdf) Gaussian assumption of the observed data. Consequently, in order to ease this restriction and preserving the previously mentioned desired char-acteristics, in this paper, we relax such Gaussian assumption and apply the weighted covariance fitting (WCF) criterion instead. The feature enhancing capabilities of the proposed WCF-based technique are corroborated via processing L-band airborne TomoSAR data of the German Aerospace Center (DLR), acquired by the F-SAR system over the for-ested test site of Froschham, Germany (2017) and L-band airborne TomoSAR data of the Jet Propulsion Laboratory (JPL) from the National Aeronautics and Space Administration (NASA), in collaboration with DLR, acquired by the UAVSAR system over the urban area of München, Germany (2015).