In this paper, we develop a synthetic aperture radar (SAR) tomography-based persistent scatterer interferometry (Tomo-PSInSAR) method to detect single and double persistent scatterers (PSs) in urban built environments. By constructing a two-tier network, we can jointly detect single and double PSs with no need for preliminary removal of the atmospheric phase screen (APS) in the whole area. This technique is more applicable in high-rise built environments (e.g., Hong Kong) with cloudy and rainy weather, where there is much uncertainty when removing the APS. In the first-tier network, we aim to detect the most reliable single PSs (SPSs) by constructing a Delaunay triangulation network. To improve the robustness of estimation, we combine beamforming with an M-estimator for parameter estimation at the arcs and introduce a ridge estimator for network adjustment. In the second-tier network, we detect the remaining SPSs and all of the double PSs (DPSs) by constructing local star networks that use the SPSs detected in the first-tier network as reference points. To simplify the detection of DPSs, we employ a local maximum ratio method for extracting overlaid DPSs. Finally, 56 Hong Kong TerraSAR-X images are used to validate the Tomo-PSInSAR method.

The conservation of World Heritage is critical to the cultural and social sustainability of regions and nations. Risk monitoring and preventive diagnosis of threats to heritage sites in any given ecosystem are a complex and challenging task. Taking advantage of the performance of Earth Observation technologies, we measured the impacts of hitherto imperceptible and poorly understood factors of groundwater and temperature variations on the monuments in the Angkor World Heritage site (400 km²). We developed a two-scale synthetic aperture radar interferometry (InSAR) approach. We describe spatial-temporal displacements (at millimeter-level accuracy), as measured by high-resolution TerraSAR/TanDEM-X satellite images, to provide a new solution to resolve the current controversy surrounding the potential structural collapse of monuments in Angkor. Multidisciplinary analysis in conjunction with a deterioration kinetics model offers new insights into the causes that trigger the potential decline of Angkor monuments. Our results show that pumping groundwater for residential and touristic establishments did not threaten the sustainability of monuments during 2011 to 2013; however, seasonal variations of the groundwater table and the thermodynamics of stone materials are factors that could trigger and/or aggravate the deterioration of monuments. These factors amplify known impacts of chemical weathering and biological alteration of temple materials. The InSAR solution reported in this study could have implications for monitoring and sustainable conservation of monuments in World Heritage sites elsewhere.

Predicting deformation is crucial to issue early warnings of abnormal conditions and implement timely remedial actions. Herein, we propose a data-driven method based on deep convolutional neural networks (DCNN) to predict interferometric synthetic aperture radar (InSAR) time-series deformation. We conducted experiments at the Hong Kong International Airport built on reclaimed lands. The results showed that the DCNN was able to predict the linear settlement of the reclaimed lands and nonlinear thermal expansion of the buildings. The mean internal error (0.3 mm) was negligible compared with the millimetre-level accuracy of the monitored deformation, indicating that the DCNN approximates the monitored deformation values very well. The root mean square error of the predicted deformation in the subsequent year was 3 mm after validation using ground data, which was comparable to the accuracy of the monitored deformation. The results demonstrated the effectiveness of the DCNN for short-term prediction of InSAR time-series deformation, which can be potentially used in early warning systems.