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Over the past decade, Interferometric Synthetic Aperture Radar (InSAR) hasbecome a successful remote sensing technique. However, during the acquisitionstep, microwave reflections received at satellite are usually disturbed bystrong noise, leading to a noisy single-look complex (SLC) SAR image. Thequality of their interferometric phase is even worse. InSAR phase filtering isan ill-posed problem and plays a key role in subsequent processing. However,most of existing methods usually require expert supervision or heavy runtime,which limits the usability and scalability for practical usages such aswide-area monitoring and forecasting. In this work, we propose a deepconvolutional neural network (CNN) based model DeepInSAR to intelligently solveboth the phase filtering and coherence estimation problems. We demonstrate ourDeepInSAR using both simulated and real data. A teacher-student framework isproposed to deal with the issue that there is no ground truth sample forreal-world InSAR data. Quantitative and qualitative comparisons show thatDeepInSAR achieves comparable or even better results than its stacked-basedteacher method on new test datasets but requiring fewer pairs of SLCs as wellas outperforms three other established non-stack based methods with lessrunning time and no human supervision.

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