Ground-based synthetic aperture radar (GBSAR) interferometry for deformation monitoring

Ph. D ThesisGround-based synthetic aperture radar (GBSAR), together with interferometry, represents a powerful tool for deformation monitoring. GBSAR has inherent flexibility, allowing data to be collected with adjustable temporal resolutions through either continuous or discontinuous mode. The goal of this research is to develop a framework to effectively utilise GBSAR for deformation monitoring in both modes, with the emphasis on accuracy, robustness, and real-time capability. To achieve this goal, advanced Interferometric SAR (InSAR) processing algorithms have been proposed to address existing issues in conventional interferometry for GBSAR deformation monitoring. The proposed interferometric algorithms include a new non-local method for the accurate estimation of coherence and interferometric phase, a new approach to selecting coherent pixels with the aim of maximising the density of selected pixels and optimizing the reliability of time series analysis, and a rigorous model for the correction of atmospheric and repositioning errors. On the basis of these algorithms, two complete interferometric processing chains have been developed: one for continuous and the other for discontinuous GBSAR deformation monitoring. The continuous chain is able to process infinite incoming images in real time and extract the evolution of surface movements through temporally coherent pixels. The discontinuous chain integrates additional automatic coregistration of images and correction of repositioning errors between different campaigns. Successful deformation monitoring applications have been completed, including three continuous (a dune, a bridge, and a coastal cliff) and one discontinuous (a hillside), which have demonstrated the feasibility and effectiveness of the presented algorithms and chains for high-accuracy GBSAR interferometric measurement. Significant deformation signals were detected from the three continuous applications and no deformation from the discontinuous. The achieved results are justified quantitatively via a defined precision indicator for the time series estimation and validated qualitatively via a priori knowledge of these observing sites.China Scholarship Council (CSC), Newcastle Universit

A Novel Phase Unwrapping Method for Low Coherence Interferograms in Coal Mining Areas Based on a Fully Convolutional Neural Network

\ua9 2008-2012 IEEE. Subsidence caused by underground coal mining activities seriously threatens the safety of surface buildings, and interferometric synthetic aperture radar has proven to be one effective tool for subsidence monitoring in mining areas. However, the environmental characteristics of mining areas and the deformation behavior of mining subsidence lead to low coherence of interferogram. In this case, traditional phase unwrapping methods have problems, such as low accuracy, and often fail to obtain correct deformation information. Therefore, a novel phase unwrapping method is proposed using a channel-attention-based fully convolutional neural network (FCNet-CA) for low coherence mining areas, which integrates multiscale feature extraction block, bottleneck block, and can better extract interferometric phase features from the noise. In addition, based on the mining subsidence prediction model and transfer learning method, a new sample generation strategy is proposed, making the training dataset feature information more diverse and closer to the actual scene. Simulation experiment results demonstrate that FCNet-CA can restore the deformation pattern and magnitude in scenarios with high noise and fringe density (even if the phase gradient exceeds π). FCNet-CA was also applied to the Shilawusu coal mining area in Inner Mongolia Autonomous Region, China. The experimental results show that, compared with the root mean square error (RMSE) of phase unwrapping network and minimum cost flow, the RMSE of FCNet-CA in the strike direction is reduced by 67.9% and 29.5%, respectively, and by 72.4% and 50.9% in the dip direction, respectively. The actual experimental results further verify the feasibility and effectiveness of FCNet-CA

Geodetic Imaging of Volcanic Deformation with Time Series Radar Interferometry

Volcanic hazards threaten millions of people in their vicinity worldwide. To mitigate the volcanic risk, we need to know which volcanoes are actively deforming and how much have they deformed. Ideally, ascending magma leads to surface uplift through elastic response, which can be precisely measured using the technique of interferometric synthetic aperture radar (InSAR) and inferred through geophysical inverse model, such as the point pressure source. In practice, the (de)pressurization process could have complex geometry in space and change nonlinearly in time, posing challenging for the deformation mapping and risk assessment afterwards. Here, I first develop algorithms to correct for phase unwrapping error in InSAR stack processing and merge them with other state-of-art algorithms to form a generic routine workflow, implement as the Miami INsar Time-series software in PYthon (MintPy). Then I demonstrate the power of this software by applying to the Kyushu Island in SW Japan using all available L-band SAR data from 1992 to 2019 and detect five out of eight actively deforming volcanoes in addition to subsidence due to anthropogenic activities. Next, I combine the radar imaging with geodetic modeling to study the shallow hydrothermal and magmatic systems in Kirishima volcanic complex during the recent unrest since 2008, covering the 2008-2010, 2011, 2017 and 2018 eruption at Shinmoe-dake and the 2018 eruption in Iwo-yama

Visualization and Localization of Interventional Devices with MRI by Susceptibility Mapping

Recently, interventional procedures can be performed with the visual assistance of MRI. However, the devices used in these procedures, such as brachytherapy seeds, biopsy needles, markers, and stents, have a large magnetic susceptibility that leads to severe signal loss and distortion in the MRI images and degrades the accuracy of the localization. Right now, there is no effective way to correctly identify, localize and visualize these interventional devices in MRI images. In this dissertation, we proposed a method to improve the accuracy of localization and visualization by generating positive contrast of the interventional devices using a regularized L1 minimization algorithm. Specifically, the spin-echo sequence with a shifted 180-degree pulse is used to acquire high SNR data. A short shift time is used to avoid severe phase wrap. A phase unwrapping method based on Markov Random Field using Highest-Confidence-First algorithm is proposed to unwrap the phase image. Then the phase images with different shifted time are used to calculate the field map. Next, L1 regularized deconvolution is performed to calculate the susceptibility map. With much higher susceptibility of the interventional devices than the background tissue, the interventional devices show positive-contrast in the susceptibility image. Computer simulations were performed to study the effect of the signal-to-noise ratio, resolution, orientation and size of the interventional devices on the accuracy of the results. Experiments were performed using gelatin and tissue phantom with brachytherapy seeds, gelatin phantoms with platinum wires, and water phantom with titanium needles. The results show that the proposed method provide positive contrast images of these interventional devices, differentiate them from other structures in the MRI images, and improves the visualization and localization of the devices

Improving Optical Qualification of Solar Concentrator by FOCuS Tool

AbstractAn improved tool for the shape qualification of parabolic trough mirror modules used in concentrated solar power plants was developed. The tool is based on the fringe reflection theory, in which sinusoidal fringe patterns are projected on a screen and their reflection over a specular surface is recorded by a camera. The observed distortions in the image are related directly to surface deviations from ideal geometry. Relevant aspects of the technique are its high spatial resolution (more than 1 million points per facet), short measurement time and easy setup. The so called FOCuS tool is capable of calculating the mirror slope deviations from its ideal design and the RMS value as a quality factor. Furthermore, the tool generates a file which can be loaded on CENER'S TONATIUH ray tracing software, through a specially developed plug-in, for mirror modeling and intercept factor calculation with several tube absorber geometries