A Robust InSAR Phase Unwrapping Method via Improving the pix2pix Network

Abstract

The main core of InSAR (interferometric synthetic aperture radar) data processing is phase unwrapping, and the output has a direct impact on the quality of the data processing products. Noise introduced from the SAR system and interferometric processing is unavoidable, causing local phase inaccuracy and limiting the unwrapping results of traditional unwrapping methods. With the successful implementation of deep learning in a variety of industries in recent years, new concepts for phase unwrapping have emerged. This research offers a one-step InSAR phase unwrapping method based on an improved pix2pix network model. We achieved our aim by upgrading the pix2pix network generator model and introducing the concept of quality map guidance. Experiments on InSAR phase unwrapping utilizing simulated and real data with different noise intensities were carried out to compare the method with other unwrapping methods. The experimental results demonstrated that the proposed method is superior to other unwrapping methods and has a good robustness to noise.

Keywords

• interferometric synthetic aperture radar
• phase unwrapping
• deep learning
• pix2pix