A U-Net Approach for InSAR Phase Unwrapping and Denoising

Author:

Vijay Kumar Sachin1,Sun Xinyao1ORCID,Wang Zheng2,Goldsbury Ryan2,Cheng Irene1ORCID

Affiliation:

1. Multimedia Research Centre, University of Alberta, Edmonton, AB T6G 2E8, Canada

2. 3vGeomatics Inc., Vancouver, BC V5Y 0M6, Canada

Abstract

The interferometric synthetic aperture radar (InSAR) imaging technique computes relative distances or surface maps by measuring the absolute phase differences of returned radar signals. The measured phase difference is wrapped in a 2π cycle due to the wave nature of light. Hence, the proper multiple of 2π must be added back during restoration and this process is known as phase unwrapping. The noise and discontinuity present in the wrapped signals pose challenges for error-free unwrapping procedures. Separate denoising and unwrapping algorithms lead to the introduction of additional errors from excessive filtering and changes in the statistical nature of the signal. This can be avoided by joint unwrapping and denoising procedures. In recent years, research efforts have been made using deep-learning-based frameworks, which can learn the complex relationship between the wrapped phase, coherence, and amplitude images to perform better unwrapping than traditional signal processing methods. This research falls predominantly into segmentation- and regression-based unwrapping procedures. The regression-based methods have poor performance while segmentation-based frameworks, like the conventional U-Net, rely on a wrap count estimation strategy with very poor noise immunity. In this paper, we present a two-stage phase unwrapping deep neural network framework based on U-Net, which can jointly unwrap and denoise InSAR phase images. The experimental results demonstrate that our approach outperforms related work in the presence of phase noise and discontinuities with a root mean square error (RMSE) of an order of magnitude lower than the others. Our framework exhibits better noise immunity, with a low average RMSE of 0.11.

Funder

NSERC

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Transformer based deep learning hybrid architecture for phase unwrapping;Physica Scripta;2024-06-21

2. GANInSAR: Deep Generative Modeling for Large-Scale InSAR Signal Simulation;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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