An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation

Phase filtering and pixel quality (coherence) estimation is critical in producing Digital Elevation Models (DEMs) from Interferometric Synthetic Aperture Radar (InSAR) images, as it removes spatial inconsistencies (residues) and immensely improves the subsequent unwrapping. Large amount of InSAR data facilitates Wide Area Monitoring (WAM) over geographical regions. Advances in parallel computing have accelerated Convolutional Neural Networks (CNNs), giving them advantages over human performance on visual pattern recognition, which makes CNNs a good choice for WAM. Nevertheless, this research is largely unexplored. We thus propose "GenInSAR", a CNN-based generative model for joint phase filtering and coherence estimation, that directly learns the InSAR data distribution. GenInSAR's unsupervised training on satellite and simulated noisy InSAR images outperforms other five related methods in total residue reduction (over 16.5% better on average) with less over-smoothing/artefacts around branch cuts. GenInSAR's Phase, and Coherence Root-Mean-Squared-Error and Phase Cosine Error have average improvements of 0.54, 0.07, and 0.05 respectively compared to the related methods.Comment: to be published in a future issue of IEEE Geoscience and Remote Sensing Letter

Remote monitoring of bridges from space

The widespread deterioration and some recent collapses of highway bridges have highlighted the importance of developing effective bridge monitoring strategies that can help identify structural problems before they become critical and endanger public safety. A typical major urban centre may possess several hundreds of bridges, which makes it difficult to instrument all these bridges with surface-mounted sensors to monitor their structural performance due to practical and economic reasons. A two-step approach may be used, in which potentially critical bridges are first identified through a screening process by remote satellite-based monitoring, and then further investigated with in-situ monitoring and detailed inspection. The capability of Canada\u2019s RADARSAT-2 advanced Synthetic Aperture Radar (SAR) satellite is being investigated for use in the first step of the proposed approach, which can help prioritize in-situ monitoring and maintenance of critical bridges. Interferometric SAR (InSAR) is an advanced processing technique applied to radar images of the Earth\u2019s surface that can detect very small movements from ground features such as infrastructure systems, including roadway and railway bridges and their major components. By applying InSAR processing techniques to a series of radar images over the same region, it is possible to detect vertical movements of infrastructure systems on the ground in the millimetre range, and therefore identify abnormal or excessive movement indicating potential problems requiring detailed ground investigation. A major advantage of this technology is that a single radar image, which can be obtained in darkness and in any weather, can cover a major urban area of up to 100 km by 100 km, and therefore all bridges in the area could be monitored cost effectively. Preliminary results from the application of this technology to transportation infrastructure assets in selected major Canadian urban centres like Vancouver and Montreal are presented and discussed.NRC publication: Ye

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

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