ARU-Net: Reduction of Atmospheric Phase Screen in SAR Interferometry Using Attention-Based Deep Residual U-Net

Abstract

Atmospheric phase screen (APS) is a very critical issue for the application of interferometric synthetic aperture radar (InSAR) techniques. The spatial-temporal variations of APS are the dominant error source in interferograms and may completely mask displacement signals. Many external meteorological data-based methods and phase-based methods have been developed in the past decades, but all have their inherent limitations. In this article, we propose a deep learning-based method, which is based on an attention-based deep residual U-shaped network (ARU-Net), to mitigate atmospheric artifacts. With this approach, APS patches and clean interferogram patches are sampled from InSAR interferograms to train the network. After training, the network can be used to mitigate the APS for individual interferograms. Compared with the generic atmospheric correction model (GACOS) and the advanced time-series InSAR method distributed scatterer interferometry (DSI), the key advantage of our method is that atmospheric delay can be effectively learned and removed from individual high-resolution interferometric phase itself without external data. Accuracy was validated by using individual and stacked interferograms from TerraSAR-X data over the Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP) sites. The results showed that our method consistently delivered greater standard deviation (SD) reduction after APS correction than the GACOS method. Moreover, the time-series results were in agreement with the DSI and leveling measurements. The effectiveness of the proposed ARU-Net to remove APS effects from interferograms shows great potential for the development of a new set of deep learning-based APS reduction methods.