Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.

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Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers.@en

Multi-temporal interferometric synthetic apertureradar (MT-InSAR) is used for many applications in earthobservation. Most MT-InSAR methods select scatterers with highcoherence throughout the entire time series. However, as timeseries lengthen, inevitable changes in surface scattering leadto decorrelation, which systematically decreases the number ofcoherent scatterers. Here, we propose a novel method to detectand process temporary coherent scatterers (TCS) by subsequentlyanalyzing the amplitude and the interferometric phase. Twohypothesis tests are developed for amplitude analysis in order toidentify the moments of appearing and/or disappearing coherentscatterers. Based on the amplitude analysis, the parametersof interest are then estimated using the interferometric phase.An optimized adaptive temporal subset approach is proposed toimprove the precision of the estimated parameters. If the scatter-ers are not evenly distributed over the area, a secondary (support)network is designed to improve the spatial point distribution.The main advantage of this method is the reliable extraction ofa subset of time series without using any contextual information.Experimental results show that the TCSs significantly increasethe number of observations for displacement monitoring andimprove the change detection capability in urban constructionareas.@en