Deep Learning-Based Branch-Cut Method for InSAR Two-Dimensional Phase Unwrapping

作者：Lifan Zhou, Hanwen Yu, Yang Lan, Mengdao Xing · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2021 · DOI：10.1109/tgrs.2021.3099997 · 被引用次数：85 · 研究领域：Synthetic Aperture Radar (SAR) Applications and Techniques、Optical measurement and interference techniques、Advanced SAR Imaging Techniques

Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the traditional 2-D PU is an ill-posed inverse problem, which means that regardless of how skillful the PU algorithm designer is, it is impossible to design an algorithm that can correctly process all the 2-D PU situations, i.e., we can only design the best PU algorithm in the statistical sense. Therefore, accumulating PU processing experience from different study cases is important for PU algorithm design. Currently, the deep learning (DL) technique provides a potential framework to accumulate processing experience, and a flood of valuable data coming from different InSAR sensors provides the ability to enable the learning-based PU technique outside the traditional model-based technique. In this article, we transform the 2-D PU problem into a learnable image semantic segmentation problem and propose a DL-based branch-cut deployment method (abbreviated as BCNet). To start, we propose the optimal branch-cut connection criterion (referred to as OPT-BC) with the reference unwrapped phase given. Next, using the relationship between the residue and branch-cut as the learning objective, BCNet is trained using the samples provided by OPT-BC to produce the branch-cut result. Finally, the traditional branch-cut method is utilized to perform the postprocessing procedure to obtain the final PU result. The experimental re...