Nonlocal Adaptive Multilooking in SAR Multipass Differential Interferometry

Filtering of interferometric synthetic aperture radar (InSAR) data has been the object of a growing number of papers, in recent years, aimed at improving the quality of differential InSAR (DInSAR) and tomography products based on multibase-line/multitemporal data. In this work, we investigate the potential of nonlocal adaptive multilooking (AML) techniques for the filtering of interferometric stacks. Nonlocal filtering has been already successfully applied to SAR data in many contexts, including the restoration of single interferograms. We propose several work-flows, with different solutions for the data preprocessing and for the estimation of similarity, at the core of all nonlocal methods. Experiments on simulated and real data, the latter both at low and high spatial resolution, prove the proposed methods to provide significant improvements over nonadaptive multilooking in terms of both resolution and detail preservation, allowing for the generation of better interferometric products.