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Open AccessArticle

DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

1
Multimedia Research Centre, University of Alberta, Edmonton, AB T6G 2E8, Canada
2
3vGeomatics Inc., Vancouver, BC V5Y 0M6, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2340; https://doi.org/10.3390/rs12142340
Received: 8 June 2020 / Revised: 17 July 2020 / Accepted: 19 July 2020 / Published: 21 July 2020
(This article belongs to the Special Issue InSAR in Remote Sensing)
Over the past decade, using Interferometric Synthetic Aperture Radar (InSAR) remote sensing technology for ground displacement detection has become very successful. However, during the acquisition stage, microwave signals reflected from the ground and received by the satellite are contaminated, for example, due to undesirable material reflectance and atmospheric factors, and there is no clean ground truth to discriminate these noises, which adversely affect InSAR phase computation. Accurate InSAR phase filtering and coherence estimation are crucial for subsequent processing steps. Current methods require expert supervision and expensive runtime to evaluate the quality of intermediate outputs, limiting the usability and scalability in practical applications, such as wide area ground displacement monitoring and predication. We propose a deep convolutional neural network based model DeepInSAR to intelligently solve both phase filtering and coherence estimation problems. We demonstrate our model’s performance using simulated and real data. A teacher-student framework is introduced to handle the issue of missing clean InSAR ground truth. Quantitative and qualitative evaluations show that our teacher-student approach requires less input but can achieve better results than its stack-based teacher method even on new unseen data. The proposed DeepInSAR also outperforms three other top non-stack based methods in time efficiency without human supervision. View Full-Text
Keywords: interferometric synthetic aperture radar (InSAR); deep learning; CNN; denseNet; phase noise reduction; coherence estimation interferometric synthetic aperture radar (InSAR); deep learning; CNN; denseNet; phase noise reduction; coherence estimation
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MDPI and ACS Style

Sun, X.; Zimmer, A.; Mukherjee, S.; Kottayil, N.K.; Ghuman, P.; Cheng, I. DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation. Remote Sens. 2020, 12, 2340.

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