Satellite radar interferometry (InSAR) has the capability of monitoring rails and embankments over wide areas. It has been demonstrated that the sub-centimeter-scale deformation of millions of InSAR measurements over railway infrastructure can be measured routinely using InSAR. Yet, to handle such huge data volumes and to recognize anomalies (such as localized differential deformation) in an efficient way, still limits the potential for operational use in wide areas, e.g. for monitoring a nation-wide railway network. In this work, we develop and demonstrate a systematic InSAR methodology that can scrutinize data and automatically detect anomalies. The method is mainly based on statistical testing theory. Particularly, we use a ‘short arc’ method to focus on detecting localized differential deformation between two nearby InSAR measurement points over the railway. Our approach is applied to the entire railway network of the Netherlands, with a total route length of more than 3000 km. We used 210 Radarsat-2 descending data from three different tracks which were acquired between 2010 and 2015. A differential deformation and anomaly map are produced. This method will be further investigated for all railways in China. This research is supported by the Young Research Scientists Support Program, in the framework of the Dragon cooperation 2013 – 2016 (Dragon 3).@en

Satellite synthetic aperture radar interferometry (InSAR) has the capability to monitor railway tracks and embankments with millimeter-level precision over wide areas. The potential of detecting differential deformation along the tracks makes it one of the most powerful and economical means for monitoring the safety and stability of the infrastructure on a weekly basis. Yet, the mere capability to detect such small deformations is not sufficient for an operational application of the technique. Handling huge data volumes, homogenizing independent datasets, and the connection with expert knowledge to identify risk areas are challenges to overcome. Here, we use a probabilistic method for InSAR time series postprocessing to efficiently scrutinize the data and detect railway instability. Moreover, to detect high-strain segments of the railway, we propose a short-arc-based method to focus on localized differential deformation between nearby InSAR measurement points. Our approach is demonstrated over the entire railway network of the Netherlands, more than 3000 km long, using hundreds of Radarsat-2 acquisitions between 2010 and 2015, leading to the first satellite-based nationwide railway monitoring system.@en

InSAR time series analysis involves the processing of extremely large datasets to estimate the relative movements of points on Earth. The estimated movements may reveal geophysical processes, or strain in anthropogenic structures. In parametric estimation methods, it is important to chose the optimal mathematical functional model relating the satellite observations to the kinematic parameters of interest. A standard approach is to parameterize the kinematic behavior, in first order, as a linear function of time, but it is unlikely that all objects behave in this purely linear way. Ideally, the kinematic parameterization should be optimized for each individual measurement point in the area of interest. In this work, following [1] we introduce a method to select the optimal functional model, with a minimum but sufficient number of free parameters using a probabilistic method based on multiple hypotheses testing.

@en

In persistent scatterer (PS) interferometry, the relatively poor 3D geolocalization precision of the measurement points (the scatterers) is still a major concern. It makes it difficult to attribute the deformation measurements unambiguously to (elements of) physical objects. Ground control points (GCP's), such as corner reflectors or transponders, can be used to improve geolocalization, but only in the range-azimuth domain. Here, we present a method which uses only one GCP, visible in only one single radar acquisition, in combination with a digital surface model (DSM) data to improve the geolocation precision, and to achieve an object snap by projecting the scatterer position to the intersection with the DSM model, in the metric defined by the covariance matrix (i.e. error ellipsoid) of every scatterer.@en

Irregular settlement of railways, either due to the loading of the trains or local ground deformation, impacts its structural stability and the safety of passengers on board. Conventional methods for structural monitoring of railway use in-situ measurements, from GPS, leveling or special survey trains. These methods are expensive and can only be applied on a limited scale, either in space or time. Moreover, they are usually only used at locations where structural deformation is suspected, requiring a-priori knowledge which may not be available everywhere. Using satellite InSAR, we are able to complement these conventional methods and monitor the kinematic behavior (deformation) of railways with millimetric precision, to detect irregular settlement. Here we use a probabilistic method for InSAR time series post-processing for the automatic detection of anomalies (e.g. railway irregular settlements). It is based on statistical hypothesis testing and the B-method of testing. In this method, we first (1) build a library of canonical kinematic functions, based on physically realistic behavior, such as linear, seasonal, temperature-related, step-wise discontinuities and exponential behavior. Then, (2) we find the best model per InSAR measurement point using multiple hypotheses testing. Particularly to detect irregular settlement of railways, the localized differential deformation between two nearby points (i.e., over ‘short arcs’) is more important for railway stability than the large deformation of certain point with respect to a far-away reference point. Therefore, we apply the testing methodology on short arcs. Finally, (3) we evaluate the quality of the estimated parameters, and classify the InSAR measurement points along the railway in terms of their temporal behavior. We conclude that irregular settlement of railways can be recognized. Since there are more than 100,000 InSAR measurement points for testing, we use the B-method of testing to increase the computational efficiency and define the optimal testing settings such as the level of significance and the power of the test. Our method is applied to all railways in the Netherlands. The kinematic time series of InSAR measurement points are derived from 73 Radarsat-2 acquisitions between June 2010 and August 2015.@en

Many areas in the world are susceptible to sinkholes and the associated risk of a sudden collapse of the surface. About 13% of the world’s land surface is covered by carbonate rocks which are sensitive to erosion by running water, leading to cavities and potentially sinkholes. In urban areas, sinkhole risks are a direct threat to human lives. Detecting sinkholes is notoriously difficult, as techniques such as ground-penetrating radar (GPR), electrical resistivity tomography (ERT), seismic methods, and microgravity, usually have a very localized range, and are difficult to deploy between buildings. Yet, recently it has been demonstrated that the detection of small depressions using radar interferometry can be indicative for imminent sinkhole collapse site identification. In an earlier study, we have demonstrated that a sinkhole occurring in the south of the Netherlands appeared to be observable as gradual deformation years before the actual collapse. Building on this experience, we designed a warning system to detect locations where the spatio-temporal behavior of the surface, or objects on the surface, has the characteristic fingerprint of a subsurface cavity, or an imminent sinkhole. We apply a robust method of hypothesis testing based on time series of TerraSAR-X and Radarsat-2 SAR data. We report on the characteristics of the method, the ways to deal with false alarms, and the potential for operational deployment.@en

Multitemporal interferometric synthetic aperture radar (InSAR) is increasingly being used for Earth observations. Inaccurate estimation of the covariance matrix is considered to be the most important source of error in such applications. Previous studies, namely, DeSpecKS and its variants, have demonstrated their advantages in improving the estimation accuracy for distributed targets by means of statistically homogeneous pixels (SHPs). However, these methods may be unreliable for small sample sizes and sensitive to data stacks showing large time spacing due to the variability of the temporal sample. Moreover, these methods are computationally intensive. In this paper, a new algorithm named fast SHP selection (FaSHPS) is proposed to solve both problems. FaSHPS explores the confidence interval for each pixel by invoking the central limit theorem and then selects SHPs using this interval. Based on identified SHPs, two estimators with respect to the despeckling and the bias mitigation of the sample coherence are proposed to refine the elements of the InSAR covariance matrix. A series of qualitative and quantitative evaluations are presented to demonstrate the effectiveness of our method.

@en

This paper presents the use of dedicated satellite radar observations to aid in railway monitoring. These radar measurements are able to detect changes in the geometry of the tracks, with up to a millimeter precision, with a bi-weekly measurement update, and a high spatial resolution by using interferometric synthetic aperture radar (InSAR) techniques. When deformation of the infrastructure is mainly limited to the vertical and the horizontal transversal direction, it is possible to derive the deformation vector in three dimensions. Therefore we developed algorithms to tune this technology to the case of railways, in order to allow for routine and near-real-time monitoring. Special emphasis is on the spatio-temporal changes in geometry. Our methods are demonstrated on a segment of the Betuweroute, a freight railway between the Rotterdam harbor in the Netherlands towards Germany, eastwards, by using 248 satellite images from the TerraSAR-X satellite acquired between 2009 and 2013, with a bi-weekly interval.@en

Conventional PSI technology is aimed towards estimating displacement time series of persistently coherent scatterers (PS) from a given set of radar acquisitions. Whenever the data from a new acquisition become available, the estimators for the parameters of interest will be computed by re-adjustment of the system of equations. This strategy of batch processing after a new acquisition is not optimal to identify changes in the behavior of single scatterer. For monitoring the structural health of buildings and civil infrastructure, there is a need for fast identification of anomalous behavior of scatterers, including the likelihood estimations of such detection results. Here we propose a general framework for the detection of anomalous behavior of (parts of) buildings and civil infrastructure by generating a sequential update of conventional interferograms, in combination with the parallel processing of the data using time series (PSI) interferometry. By estimating and analyzing the phase change per arc from each wrapped interferogram, abnormal changes can be detected fast and reliably. Our approach is demonstrated on a near-collapse of a building in Heerlen, the Netherlands, using Radarsat-2 data.

@en

Persistent Scatterer Interferometry (PSI) has emerged over the last decade as a technique capable of very accurate (millimetric) measurements of ground deformation occurring at radar scatterers (persistent scatterers or PS) that are phase coherent over a period of time. PSI studies using C-band SAR data have shown that the PS spatial density in urban areas is usually very high (100-300 PS/km2). However, many ground deformation phenomena (e.g. tectonic motion, volcanoes, landslides, mining, gas extraction, CO2 sequestration) occur in uninhabited or rural areas with few man-made structures, leading to much lower PS density because of significant phase decorrelation between subsequent SAR acquisitions. In order for PSI to be effective in monitoring these areas, it has been found that a PS density greater than about 10 PS/km2 is required. Artificial amplitude- and phase-stable radar scatterers may thus have to be introduced in non-urbanised geodynamic areas that have too low a density of PS points. Conceptually the simplest of these artificial PS points are corner reflectors. Several experiments have been performed in the past using these reflectors, with conclusive results about their amplitude and phase stability. They suffer, however, from the disadvantage of large size (in the order of a metre in case of C-band SAR). To make these artificial PS points easy to deploy and maintain, especially in poorly accessible areas, Compact Active Transponders (CATs) have been designed to be used in lieu of corner reflectors. These CATs are small (in the order of a few tens of centimetres), lightweight (<3 kg), less obtrusive, and have the added advantage of a better link budget due to signal amplification by the transponder. They are sealed, function autonomously with internal power and over a wide temperature range, and can operate unattended for more than a year. Additionally, since a CAT is transmitter-specific and is only turned on at the time of the satellite overpass, it offers little interference to other radar or radio targets. However, it is of paramount importance in geodetic applications to ensure that the phase of the CAT remains stable in all operating and environmental conditions. Towards this goal, an experiment to validate the phase stability of CATs has been set up in a farmland in Delft (The Netherlands). The setup comprises three CATs and three corner reflectors, which are installed at distances of a couple of hundred metres from each other. SAR data from the ERS-2 Ice-Phase Mission are being acquired every three days between March and June 2011. Since the area does not exhibit steady ground deformation, some of the units are displaced vertically by a controlled amount. Levelling is performed between the CATs and the corner reflectors as close as possible to each SAR acquisition, in order to validate the height differences obtained from the radar phase information. As a second means of validation, campaign-style GPS is performed on each of the devices to accurately position them in WGS-84 coordinates. One of the CATs in the Delft field experiment has an integrated GPS antenna, to ensure millimetric coregistration and a coherent cross-reference. This novel unit called I2GPS (Integrated Interferometry and GNSS for Precision Survey) has been developed with the objective of producing a fully-integrated deformation map. In addition to providing absolute calibration for PSI data, the high temporal sampling rate of GPS data imparts the capability of accurately detecting abrupt ground motion in three dimensions. With adequate GPS/I2GPS units, the vertical components of the local velocity field can be derived from single-track InSAR line-of-sight displacements. The results and conclusions of this experiment consisting of corner reflectors, CATs and I2GPS will be presented and analysed here.@en

Detecting a point-like target when it is horizontally displaced is of paramount importance in target tracking and in measuring the motion of glaciers over short intervals of time. This paper performs an experimental study of the accuracy, precision and sensitivity of the horizontal motion detectable using SAR. Therefore point-like targets such as corner reflectors (CR) are moved horizontally in a controlled manner over short time intervals to reproduce the real target motion. Such CR movements are monitored using SAR and the results are compared with the ground truth to arrive at the target horizontal motion determination parameters. Towards this goal three CRs were installed each displaced by a few hundreds of metres in a farmland in Delft, The Netherlands. These corner reflectors are inclined for ERS-2 3-days ice-phase mission starting March 2011. Since the area does not exhibit horizontal motion, one of the CRs was moved horizontally stepwise in the order of a few centimeters to a few metres. At each step the CRs are imaged by SAR and also measured by campaign-style GPS (and with a few leveling campaigns) in order to provide the actual displacement in three dimensions. Then the motion is computed using SAR data and results are compared with the GPS measurements to validate the sensitivity of SAR in detection of motion of the targets. The experimental setup is such that the CRs are visible starting from March 2011 from both ascending and descending orbit TerraSAR-X satellite acquisitions over Delft. Hence similar parameters such as sensitivity, precision and accuracy of motion detection will be derived for X-band SAR as well. Further, in order to substantially verify the reliability of our computations, data from three different field experiments with stable CRs performed with ERS-1/2 in 1996, with ENVISAT from 2003 to 2007 and with ENVISAT from March 2010 to January 2011 in the areas of Groningen, Delft and Cabauw respectively were exploited. The outcome of our experiment will result in the empirical study of the sensitivity of motion detection of point-like targets in C- and X- bands. Also the influence of these parameters under varying imaging conditions such as change in Doppler and perpendicular baselines will be discussed. @en