Deep-Learning-Based Mask-Cut Method for InSAR Phase Unwrapping

Abstract

Two-dimensional phase unwrapping (2D-PU) is a key processing step for Interferometric Synthetic Aperture Radar (InSAR) and it plays an important role in InSAR data processing. For the phase unwrapping problem, many scholars began to consider using the deep learning (DL) technology in the field of artificial intelligence. By accumulating InSAR PU processing experience through deep learning, the learning-based PU method can surpass the traditional PU algorithm sometimes. Therefore, this paper designs a mask-cuts (MC) deployment network based on deep learning, which is named as MCNet, and the PU method based on this network is also known as MCNet-PU. First, the residues images and its corresponding mask-cuts images are obtained by using the traditional MC method as the training data and testing data; Secondly, the relationship between residues and mask-cuts is learned through the training of the self-built MCNet; Then, the trained MCNet is used to obtain the mask-cuts corresponding to the interferogram to be unwrapped. Finally, the unwrapped result is obtained by phase integration using the traditional flood fill method. Compared with the traditional MC method, MCNet does not need to use the quality map to guide the deployment of the mask-cuts, nor does it need to refine the mask-cuts, and it can make the deployment of the mask-cuts more accurate. Experiments on simulated and real InSAR data show that the MCNet-PU method can improve the phase unwrapping success ratio (PUSR) by about 4% 15%, which shows the effectiveness of the method.