A Probabilistic Approach for InSAR Time-Series Postprocessing

Abstract

Monitoring the kinematic behavior of enormous amounts of points and objects anywhere on Earth is now feasible on a weekly basis using radar interferometry from Earth-orbiting satellites. An increasing number of satellite missions are capable of delivering data that can be used to monitor geophysical processes, mining and construction activities, public infrastructure, or even individual buildings. The parameters estimated from these data are used to better understand various natural hazards, improve public safety, or enhance asset management activities. Yet, the mathematical estimation of kinematic parameters from interferometric data is an ill-posed problem as there is no unique solution, and small changes in the data may lead to significantly different parameter estimates. This problem results in multiple possible outcomes given the same data, hampering public acceptance, particularly in critical conditions. Here, we propose a method to address this problem in a probabilistic way, which is based on multiple hypotheses testing. We demonstrate that it is possible to systematically evaluate competing kinematic models in order to find an optimal model and to assign likelihoods to the results. Using the B-method of testing, a numerically efficient implementation is achieved, which is able to evaluate hundreds of competing models per point. Our approach will not solve the nonuniqueness problem of interferometric synthetic aperture radar (InSAR), but it will allow users to critically evaluate (conflicting) results, avoid overinterpretation, and thereby consolidate InSAR as a geodetic technique. Index Terms-B-method of testing, deformation modeling, multiple hypotheses testing (MHT).

Ground Stability Monitoring of Undermined and Landslide Prone Areas by Means of Sentinel-1 Multi-Temporal InSAR, Case Study from Slovakia

“…Assuming the detailed analysis of PS kinematic time series along with routine updates of deformation maps, those could contribute to development of early warning systems. Thus the classification of deformation trends based on conditional sequence of statistical tests (as proposed by e.g., [41] or [42]) is among the planned steps for respective case studies. Since both case study deformation phenomena can still be considered substantially active and a number of buildings are subject to continual damage, assessment of remediation works and operational routine monitoring is of utmost importance.…”

InSAR datum connection using GNSS-augmented radar transponders

“…In reality, however, the reference point is also a scatterer, with thermal noise and a certain unknown coherence per epoch, which we term as the reference point noise (RPN) (Chang and Hanssen 2016). If not accounted for, the RPN manifests in the time-series of every other PS, in addition to their own noise.…”

“…due to atmospheric or orbital errors. We isolate and remove the RPN as follows (Chang and Hanssen 2016):…”

Applicability of satellite radar imaging to monitor the conditions of levees

“…Even though two subsequent radar images are sufficient to create an interferogram, a longer time period (days to weeks, based on the precision of the observations and the satellite characteristics) may be required to be able to reliably detect an anomaly. Time‐series analysis methods, for example, hypothesis testing (Chang & Hanssen, ) or machine learning techniques (van de Kerkhof, Pankratius, Chang, van Swol, & Hanssen, ) could be used to automate the assessment. Moreover, as temporal and spatial resolution of satellite observations is expected to increase further, it may become possible in the future to observe geotechnical failures directly.…”

“…Even though two subsequent radar images are sufficient to create an interferogram, a longer time period (days to weeks, based on the precision of the observations and the satellite characteristics) may be required to be able to reliably detect an anomaly. Timeseries analysis methods, for example, hypothesis testing (Chang & Hanssen, 2016) as temporal and spatial resolution of satellite observations is expected to increase further, it may become possible in the future to observe geotechnical failures directly. The potential of using SAR data can be explained by considering both the problem-driven and data-driven approaches.…”