On the analysis and patterns of persistent scatterer interferometry results for satellite-based deformation monitoring

Abstract

The remote sensing method Persistent Scatterer Interferometry (PSI) has developed in the last two decades into a tool for monitoring deformations of the earths surface. Hereby it can be applied to large areas and scenes like earth quakes, landslides or sinkholes but the increasing availability of high resolution Synthetic Aperture Radar (SAR) data also enables a monitoring of small scenes like individual buildings. PSI is recognized and appreciated in the remote sensing community and its benefit has been proven in countless applications. The PSI principle is based on the evaluation of time series of coherent SAR satellite images and considers the relative phase change over time for individual pixels. As a result of this interferometric evaluation, time series are obtained, which capture line-of-sight (LOS) movement of a scatterer over time with millimetre accuracy. The PSI method is especially suited for urban areas, because of the high density of good radar back scatterers in these locations. For high-resolution SAR data, such as those acquired by the TerraSAR-X mission, millions of such so-called Persistent Scatterer (PS) points and their deformation time series can be obtained. The presentation, evaluation, and interpretation of such data is still a challenge. The here presented research contributes to the question of how the joint analysis of many PS points and their time series can be used to infer the underlying causes of the deformation. The investigation of such a field of time series helps in the understanding of temporal and spatial patterns in movements. A distinction is made between the analysis of large-scale areas and the consideration of points on individual buildings. For wide-area deformations, such as those caused by underground constructions, mining activities or by undermining groundwater flow, adapted methods from meteorology, interpolation and decomposition procedures of different observation geometries are presented and discussed. For the monitoring of individual structures, such as single buildings, methods were developed that combine SAR data and geo-data from other sources, such as Airborne Laser Scanning (ALS) data and crowd sourced building circumferences. It can be shown that by grouping PS points that have correlated motion patterns, a building can be segmented into its statically independently moving elements. To achieve such a clustering in a robust way, so it can be applied to different data sources, a non-linear dimension reduction based on a hybrid distance metric is introduced. The results from such a clustering can then be integrated into detailed 3D models, such as those available for Building Information Modeling (BIM) based construction processes, and thus offer the possibility of a continuous and efficient structural monitoring of a building. Often PSI results have to be communicated to experts from non-SAR affine fields such as civil- and geo- engineering for interpretation, which can be challenging without specialized software. For this purpose, exemplary web portals are presented here, that allow PSI results to be displayed interactively. Such platforms are addressing the specific complexity of PSI data, so that informed decisions can be made. The utility of an ensemble evaluation of many PSI time series can be demonstrated, as it proves beneficial in wide-area processes. Motion patterns become identifiable and their spatial propagation can undergo analysis. When considering PS points on single buildings, a grouping of points based on their deformation patterns leads to redundant measurement and segments a structure into its independently moving parts. This segmentation can then be integrated into existing 3D building models and industry standards, signifying an important advancement towards automated and city-wide risk assessment of buildings. Web-based analysis platforms, specifically tailored for the SAR data, serve as a decision support system (DSS) and aid in sharing the findings with non-SAR experts.