A novel two-stage adversarial joint learning model for reconstructing InSAR phase in decorrelated areas

Interferograms are basic observables of any Interferometric Synthetic Aperture Radar (InSAR) measurements. Interferometric decorrelation, however, often reduces the quality of interferograms, sometimes to an extent where no interferometric measurements can be properly carried out. Techniques such as applying a filter can help in reducing the impact of noise in interferograms but often cannot overcome the problem of decorrelation satisfactorily. This paper presents an approach based on a novel two-stage generative adversarial network (GAN) tailored for reconstructing interferometric phase values in decorrelated areas. The approach comprises an edge mapping stage (EMS) and a phase predicting stage (PPS). During the edge mapping stage, a pre-trained convolutional neural network (CNN) identifies fringe lines, while a GAN reconnects the discontinuous fringes. In the phase predicting stage, a second GAN uses the reconnected fringes as a guide to reconstruct the phase information. The model was trained on simulated datasets, achieving an overall accuracy (OA) of 84% in fringe reconnection and a structural similarity index (SSIM) of 96%. We validated the proposed model with real-world case studies, successfully reconstructing the phases of co-seismic deformation interferograms for the Tonopah, Nevada earthquake (M 6.5, May 15, 2020) and the Western Xizang earthquake (M 6.3, July 22, 2020). We also evaluated the adaptability of the proposed model using topographic mapping datasets. The experimental results achieved a cross-correlation range of 0.72 – 0.87 when reconstructing phase information over the Greater Bay Area (GBA) with fine-tuning, indicating potential applicability of the approach to a broader range of InSAR applications. • Interferometric phase reconstructed in decorrelation areas for the first time • The GAN model is deployed to achieves 84% overall accuracy in fringe reconnection • The model works effectively for different applications through fine-tuning