Statistical Regularization for TomoSAR Imaging With Multiple Polarimetric Observations

Abstract

The polarimetric versions of focusing techniques for synthetic aperture radar (SAR) tomography (TomoSAR), apart from estimating the pseudopower and retrieving the height of reflectors from the recovered local maxima, allow extracting the associated scattering mechanisms. Additionally, scattering patterns can be examined by means of polarimetric indicators like alpha mean angle, used to associate observables with physical properties of the medium. Aimed at easing the analysis of the scattering processes occurring in the illuminated scene, this article extends the weighted covariance fitting (WCF) based iterative spectral estimator (WISE) to the polarimetric configuration, called hereafter PolWISE. The addressed technique attains finer resolution than conventional methods like PolCapon, performing suppression of artefacts and ambiguity reduction. PolWISE is a statistical regularization approach, which reduces the TomoSAR inverse problem to the selection of a regularization parameter, chosen via the L-Curve method. Furthermore, being PolWISE an iterative technique, under/over regularization is prevented by terminating the procedure at an appropriate iteration. A stopping rule based on Kullback–Leibler information criterion is employed. The PolWISE algorithm is assessed thoroughly through simulations and experiments on fully polarimetric TomoSAR airborne data at L-band, acquired from an urban scenario.