Persistent Scatterer Interferometry (PSI) is a time series remote sensing technique to estimate displacements of geo-objects from the interferometric phases of selected Persistent Scatterers (PS). The relative position of a scatterer within a resolution cell causes an additional phase contribution in the observed phase, which needs to be accounted for in PSI processing. Here we analyze the influence of this sub-pixel position correction on point localization and displacement quality. Apart from a theoretical evaluation, we perform experiments with TerraSAR-X, Radarsat-2, and Sentinel-1, demonstrating various levels of improvement. We show that the influence of the sub-pixel correction is significant for the geolocation of the scatterer (meter-level improvement), modest for the elevation estimation (centimeter-level improvement), and limited for the displacement estimation (submillimeter-level). For displacement velocities, we find variations of a few tenths of a millimeter per year. The effect of sub-pixel correction is most dominant for large orbital baselines and short time series.

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Multi-epoch Synthetic Aperture Radar Interferometry (InSAR) is widely used to estimate displacements of selected scatterers from phase observations. However, their interpretation needs a connection to objects in the real world. To associate InSAR scatterers to their corresponding geo-objects, it is necessary to (i) accurately estimate the phase center of radar scatterers in radar coordinates, (ii) precisely position the scatterers in 3D geographic coordinates, and (iii) satisfy the constraint that these positions need to be physically realistic. This study addresses these three requirements. The effective phase center of a scatterer is not situated at the nominal position of the pixel. As a result, scatterers are evaluated at the wrong position and the reference phase calculated at that location will be biased. We evaluate the influence of this sub-pixel position on the geolocation of the scatterer and its deformation quality for various satellite platforms. A method to locate the phase center of the dominant scatterer is developed and is applied to a stack of TerraSAR-X, Radarsat-2, and Sentinel-1 images. The sub-pixel correction shows to be significant for improving the geolocation, up to a few meters—especially for planar (horizontal) precision. It is only of limited influence for the displacement estimation and more relevant in the case of large orbital baselines. Even after sub-pixel correction, the position of scatterers in an earth-centered, earth-fixed geodetic datum is often in order of a few meters, which is not always sufficient to physically link the scatterer to a geo-object. We evaluate four approaches for correcting this positioning bias,i.e., (i) an advanced geophysical correction, (ii) the single-epoch deployment of a corner reflector (CR), (iii) a multi-epoch deployment of a CR, and (iv) a correction using a high-precision digital surface model (DSM). The positioning performance of these approaches is analyzed from the aspects of practicability, reliability, and precision with TerraSAR-X and Sentinel-1 data. We show that while the multi-epoch CR approach achieves the best positioning results, the DSM-assisted correction is able to obtain comparable results if a high precision DSM is available, better than DTED-4. The position of the estimated geometric phase center may differ from the position of the physical phase center. We use ray-tracing to predict the position of point scatterers using generic 3D models, and match them with the detected point scatterers from a stack of TerraSAR-X images. We find that the majority of detected scatterers appears to be positioned at their correct physical location. Moreover, many point scatterers correspond to multiple scattering mechanisms—more than half of the identified scatterers correspond to double- or triple-bounce scatterers. The mismatch between the geometrically estimated position and the signal source occurs mainly for multiple scattering: fourfold and more. This shows that the bounce levels of the scatterers are a relevant attribute to understand and interpret the displacements of persistent scatterers. In general, we conclude that sub-pixel correction and positioning bias correction should be included in default InSAR data processing, and that the majority of detected scatterers are positioned at physically realistic locations.@en

To correctly interpret the estimated displacements in InSAR point clouds, especially in the built environment, these need to be linked to real-world structures. This requires the accurate and precise 3D positioning of each point. Artificial ground control points (GCPs), such as corner reflectors, serve this purpose, but since they require efforts and resources, there is a need for criteria to assess their usefulness. Here we evaluate the value and necessity of using GCPs for different scenarios, concerning the required efforts, and compare this to alternatives such as digital surface models (DSM) and advanced (geo) physical corrections. We consider single-epoch as well as multi-epoch GCP deployment, reflect on the number of GCPs required in relation to the number of SAR data acquisitions, and compare this with digital surface models of different quality levels. Analyzing the geolocation performance using TerraSAR-X and Sentinel-1 data, we evaluate the pros and cons of various deployment options and show that the multi-epoch deployment of a GCP yields optimal geolocalization results in terms of precision, accuracy, and reliability.@en

Persistent scatterers (PSs) are coherent measurement points obtained from time series of satellite radar images, which are used to detect and estimate millimeter-scale displacements of the terrain or man-made structures. However, associating these measurement points with specific physical objects is not straightforward, which hampers the exploitation of the full potential of the data. We have investigated the potential for predicting the occurrence and location of PSs using generic 3-D city models and ray-tracing methods, and proposed a methodology to match PSs to the pointlike scatterers predicted using RaySAR, a ray-tracing synthetic aperture radar simulator. We also investigate the impact of the level of detail (LOD) of the city models. For our test area in Rotterdam, we find that 10% and 37% of the PSs detected in a stack of TerraSAR-X data can be matched with point scatterers identified by ray tracing using LOD1 and LOD2 models, respectively. In the LOD1 case, most matched scatterers are at street level while LOD2 allows the identification of many scatterers on the buildings. Over half of the identified scatterers easily correspond to identify double or triple-bounce scatterers. However, a significant fraction corresponds to higher bounce levels, with approximately 25% being fivefold-bounce scatterers.@en

Temperature variation plays a significant role in the long-term structural behaviour of civil infrastructures, but very few quantitative studies have measured and analysed the infrastructures' global thermal dilation because of their large sizes and geometric complexities. The modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique has great potential in applications of their thermal dilation mapping and characterization due to the techniques' unique capabilities for use in large areas, with high-resolution, and at low-costs for deformation measurements. However, the practical application of DInSAR in thermal dilation estimation is limited by difficulty in the precise separation from the residual topographic phase and the trend deformation phase. Moreover, due to a lack of thermal dilation characteristics analyses in previous studies, the thermal dilation mechanisms are still unclear to users, which restricts the accurate understanding of the thermal dilation evolution process. Given the above challenges, an advanced multi-temporal DInSAR approach is proposed in this study, and the effectiveness of this approach was presented using three cases studies concerning different infrastructure types. In this method, the coherent, incoherent, and semantic information of structures were combined in order to refine the detection of point-like targets (PTs). Interferometric subsets with small temporal baselines and temperature differences were used for high-resolution topography estimation. A pre-analysis was adopted to determine the transmission direction, spatial pattern, and temporal variation of the thermal dilation. Then, both the traditional least squares estimation and our robust coherence-weighted least squares regression analysis were performed between the time series displacements and the corresponding temperatures to quantitatively estimate the thermal dilation model. The results were verified in terms of the estimated linear thermal dilation coefficient, which indicates the greater reliability of our method. Furthermore, the thermal dilation characteristics of different civil infrastructure types were analysed, which facilitates a greater understanding of the thermal dilation evolution process of civil infrastructures.

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Large-scale reclamation projects during the past decades have been recognized as one of the driving factors behind land subsidence in coastal areas. However, the pattern of temporal evolution in reclamation settlements has rarely been analyzed. In this work, we study the spatio-temporal evolution pattern of Linggang New City (LNC) in Shanghai, China, using space-borne synthetic aperture radar interferometry (InSAR) methods. Three data stacks including 11 X-band TerraSAR-X, 20 L-band ALOS PALSAR, and 35 C-band ENVISAT ASAR images were used to retrieve time series deformation from 2007 to 2010 in the LNC. An InSAR analysis from the three data stacks displays strong agreement in mean deformation rates, with coefficients of determination of about 0.9 and standard deviations for inter-stack differences of less than 4 mm/y. Meanwhile, validations with leveling data indicate that all the three data stacks achieved millimeter-level accuracies. The spatial distribution and temporal evolution of deformation in the LNC as indicated by these InSAR analysis results relates to historical reclamation activities, geological features, and soil mechanisms. This research shows that ground deformation in the LNC after reclamation projects experienced three distinct phases: primary consolidation, a slight rebound, and plateau periods.

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Arch bridges are important transportation infrastructures widely distributed in China, but they are prone to structural defects due to aging without routine inspection and maintenance. Therefore, Structural Health Monitoring (SHM) of these bridges is urgently needed by civil engineers to effectively reduce the risk of bridge damage or collapse on public safety. An essential method for SHM, the modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, can detect subtle deformation of bridges at relatively low costs. Nevertheless, identifying dense point-like targets (PTs) on such partially coherent arch bridges in SAR image is more difficult than that for other man-made objects, owing to their complex structures and backscattering behaviors. Furthermore, the complex mechanical properties of arch bridges, due to the varying arch-beam interactions, make it hard to separate the surface deformation and thermal dilation accurately, and the lack of specific structural knowledge, that can help to understand the deformation evolution process, limits the global structural risk assessment. Aiming at these problems, we developed a structure-driven multi-temporal DInSAR approach for arch bridge-specific SHM. By introducing three structure-driven steps, i.e. backscattering geometrical interpretation, linear thermal dilation estimation and validation, and Deformation Feature Points (DFPs) based risk assessment, into the traditional DInSAR method, the reliability of PTs identification, thermal dilation separation, and structural risk assessment for arch bridges are significantly improved. The effectiveness of our approach was fairly presented by two case studies of the Rainbow and Lupu bridges, and the experimental results were verified by leveling benchmark validation, cross-sensor comparison, as well as structural-reliability assessment. Our results revealed that arch bridges exhibit a similar pattern of PTs distribution that is dense around piers and sparse on the spans, as well as a symmetrical progressive pattern of surface deformation with the subsidence increasing from piers and reaching a peak at the central spans. In contrast, magnitudes and mechanisms of thermal dilation are different, and highly dependent on the materials and structural characteristics of specific bridges.

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The geolocation of coherent radar scatterers, used for InSAR deformation analysis, is often not accurate enough to associate them to physical geo-objects. The imaging geometry of satellite InSAR results in (i) biases in the entire point field, and (ii) quite elongated and skewed confidence ellipsoids in the range, azimuth and cross-range direction. The metric defined by the covariance matrix of the InSAR results defines the optimal way to associate scatterers with geo-objects. Laser scanning point clouds, stemming from aerial or terrestrial laser surveys, yield very dense geometry of geo-objects and topography. Here we combine InSAR and laser point clouds, taking the covariance metrics of the InSAR data into account. This enables us to correct the positions of InSAR data, to provide a geometric match with geo-objects. We demonstrate how this allows for adding contextual information as attributes to individual scatterers, which improves the interpretation of the InSAR results. @en

Thermal dilation is a vital component of deformation along the extensive railway network infrastructure. To monitor subtle deformation, the synthetic aperture radar interferometry (InSAR) technique has been adopted as a space-borne geodetic tool. However, InSAR applications in railway stability surveillance have been largely limited by the sparseness of detectable point-like targets (PTs). Moreover, only one-dimensional linear displacements in radar line-of-sight direction can be measured by a single data stack. To address these issues, we developed an improved persistent scatterers InSAR approach that can retrieve thermal dilation effects with an increased number of PTs along the railways. This proposed strategy effectively combines SAR amplitude, interferometric phase, and the spatial information of railway structures to maximize the number of PTs. A least square fitting of the residual phase obtained by iterative spatial-temporal filtering with respect to temperature difference is used to estimate the thermal dilation of metal and concrete-asphalt materials. To validate the effectiveness of this approach, case studies using ENVISAT ASAR (ASAR) and TerraSAR-X (TSX) datasets were carried out on the railways of Beijing–Tianjin, Beijing–Shanghai, and Shanghai–Hangzhou. Subsidence velocity, gradient, and thermal dilation were used to identify hazardous grades along each railway. Furthermore, linear deformation rates in two dimensions, i.e., vertical and west-east directions, along Shanghai–Hangzhou Railway were inverted from ascending ASAR and descending TSX observations to reveal track conditions at a high level of detail.

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Since the Persistent Scatterer Synthetic Aperture Radar (SAR) Interferometry (PSI) technology allows the detection of ground subsidence with millimeter accuracy, it is becoming one of the most powerful and economical means for health diagnosis of major transportation infrastructures. However, structures of different types may suffer from various levels of localized subsidence due to the different structural characteristics and subsidence mechanisms. Moreover, in the complex urban scenery, some segments of these infrastructures may be sheltered by surrounding buildings in SAR images, obscuring the desirable signals. Therefore, the subsidence characteristics on different types of structures should be discussed separately and the accuracy of persistent scatterers (PSs) should be optimized. In this study, the PSI-based subsidence mapping over the entire transportation network of Shanghai (more than 10,000 km) is illustrated, achieving the city-wide monitoring specifically along the elevated roads, ground highways and underground subways. The precise geolocation and structural characteristics of infrastructures were combined to effectively guide more accurate identification and separation of PSs along the structures. The experimental results from two neighboring TerraSAR-X stacks from 2013 to 2016 were integrated by joint estimating the measurements in the overlapping area, performing large-scale subsidence mapping and were validated by leveling data, showing highly consistent in terms of subsidence velocities and time-series displacements. Spatial-temporal subsidence patterns on each type of infrastructures are strongly dependent on the operational durations and structural characteristics, as well as the variation of the foundation soil layers.

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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