Satellite-based persistent scatterer satellite radar interferometry facilitates the monitoring of deformations of the earth's surface and objects on it. A challenge in data acquisition is the handling of large numbers of coherent radar scatterers. The behavior of each scatterer is time dependent and is influenced by changes in deformation and other phenomena. Built environments are especially challenging since scatterers may have different signal qualities and deformations may vary significantly among objects. Thus, the estimation of the actual deformation requires a functional model and a stochastic model, both of which are typically unknown per scatterer and observation. Here, we present an approach that models the deformation behavior for each individual scatterer. Our technique is applied in a postprocessing phase following the state-of-the-art interferometric processing of persistent scatterers. This addition significantly improves the interpretation of large data sets by separating the relevant phenomena classes more efficiently. It leverages more information than other methods from individual scatterers, which enhances the quality of the estimation and reduces residuals. Our evaluation shows that this technique can discriminate objects in terms of similar deformation characteristics that are independent of the specific spatial position and temporal complexity. Future applications analyzing large data sets collected by satellite radars will, therefore, drastically benefit from this new capability of extracting categorized types of time series behavior. This contribution will augment traditional spatial and temporal analysis and improve the quality of time-dependent deformation assessments.

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.

Unexpectedly occurring sinkholes caused by shallow submerged voids are one of the key problems of intensively built-up post-mining areas. Five percent of Poland’s territory is still under the influence of post-mining deformation. Impending sinkholes, which tend to develop randomly over very large areas, cannot be detected by using traditional geophysical methods. Moreover, geodetic measurement methods like Global Positioning System (GPS) analyses, tachymetry, laser scanning, or photogrammetry may be useful only for the registration of the dimensions and locations of sinkholes that have already occurred. Here, we investigate an area in Upper Silesia, Poland, where 345 sinkholes were recorded over a period of more than 20 years (1992–2013). Most of the events occurred in intensively built-up areas, and thus, these sinkholes posed a direct threat to the population. In the test area, 11 sinkholes were detected with a maximum depth of 16 m and maximum dimension of 25 m. The root cause of sinkhole formation was the collapse of post-mining shallow voids. We used satellite radar technology to detect and monitor ground movements potentially associated with the impending sinkholes. The findings showed that the application of Persistent Scatterer Interferometry can support the identification of zones where sinkholes will occur, given adequate spatio-temporal sampling. A stack of Envisat Synthetic Aperture Radar (SAR) images acquired between March 2003 and August 2010 was used in the analysis, and the results confirmed that precursory ground movements were detectable at an early stage of sinkhole development. The land subsidence rate observed prior to sinkhole collapse was not constant in time. Accelerated ground movements within 100 m of an observed sinkhole were detected. We conclude that satellite measurements may provide significant support in the early identification of areas prone to sinkhole occurrence.

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.