A Joint Markov Random Field Approach for SAR Interferogram Filtering and Unwrapping

This paper presents a new integrated interferometric synthetic aperture radar (InSAR) phase filtering and unwrapping method based on a Markov random field (MRF) model. This approach aims to estimate a noiseless unwrapped phase from the observed noisy interferogram. The phase image is modeled using a joint MRF with a corresponding energy function related simultaneously to noise filtering and phase unwrapping (PU). This function contains two parts: the first is for interferogram filtering process and the second aims to unwrap this filtered phase. The filtered unwrapped phase image is estimated by minimizing the proposed energy function using the genetic algorithm (GA). The proposed approach is tested and validated on simulated and real interferograms acquired with the Radarsat-2, ERS-2, and Envisat satellites acquired over two distinct regions in Tunisia (Mahdia and Ben Guerden) with different geographical characteristics. The obtained results show a significant improvement with respect to other simultaneously filtering and unwrapping algorithms.