Remote sensing radar satellites cover wide areas and provide spatially dense measurements, with millions of scatterers. Knowledge of the precise position of each radar scatterer is essential to identify the corresponding object and interpret the estimated deformation. The absolute position accuracy of synthetic aperture radar (SAR) scatterers in a 2D radar coordinate system, after compensating for atmosphere and tidal effects, is in the order of centimeters for TerraSAR-X (TSX) spotlight images. However, the absolute positioning in 3D and its quality description are not well known. Here, we exploit time-series interferometric SAR to enhance the positioning capability in three dimensions. The 3D positioning precision is parameterized by a variance-covariance matrix and visualized as an error ellipsoid centered at the estimated position. The intersection of the error ellipsoid with objects in the field is exploited to link radar scatterers to real-world objects. We demonstrate the estimation of scatterer position and its quality using 20 months of TSX stripmap acquisitions over Delft, the Netherlands. Using trihedral corner reflectors (CR) for validation, the accuracy of absolute positioning in 2D is about 7 cm. In 3D, an absolute accuracy of up to B Prabu Dheenathayalan ∼66 cm is realized, with a cigar-shaped error ellipsoid having centimeter precision in azimuth and range dimensions, and elongated in cross-range dimension with a precision in the order of meters (the ratio of the ellipsoid axis lengths is 1/3/213, respectively). The CR absolute 3D position, along with the associated error ellipsoid, is found to be accurate and agree with the ground truth position at a 99 % confidence level. For other non-CR coherent scatterers, the error ellipsoid concept is validated using 3D building models. In both cases, the error ellipsoid not only serves as a quality descriptor, but can also help to associate radar scatterers to real-world objects.
In recent years, synthetic aperture radar interferometry has become a recognized geodetic tool for observing ground motion. For monitoring areas with low density of coherent targets, artificial corner reflectors (CRs) are usually introduced. The required size of a reflector depends on radar wavelength and resolution and on the required deformation accuracy. CRs have been traditionally used to provide a high signal-to-clutter ratio (SCR). However, large dimensions can make the reflector bulky, difficult to install and maintain. Furthermore, if a large number of reflectors are needed for long infrastructure, such as vegetation-covered dikes, the total price of the reflectors can become unaffordable. On the other hand, small reflectors have the advantage of easy installation and low cost. In this paper, we design and study the use of small reflectors with low SCR for ground motion monitoring. In addition, we propose a new closedform expression to estimate the interferometric phase precision of resolution cells containing a (strong or weak) point target and a clutter. Through experiments, we demonstrate that the small reflectors can also deliver displacement estimates with an accuracy of a few millimeters. To achieve this, we apply a filtering method for reducing clutter noise.
Associating a radar scatterer to a physical object is crucial for the correct interpretation of interferometric synthetic aperture radar measurements. Yet, especially for medium-resolution imagery, this is notoriously difficult and dependent on the accurate 3-D positioning of the scatterers. Here, we investigate the 3-D positioning capabilities of ENVISAT medium-resolution data. We find that the data are perturbed by range-and-epoch-dependent timing errors and calibration offsets. Calibration offsets are estimated to be about 1.58 m in azimuth and 2.84 m in range and should be added to ASAR products to improve geometric calibration. The timing errors involve a bistatic offset, atmospheric path delay, solid earth tides, and local oscillator drift. This way, we achieve an unbiased positioning capability in 2-D, while in 3-D, a scatterer was located at a distance of 28 cm from the true location. 3-D precision is now expressed as an error ellipsoid in local coordinates. Using the Bhattacharyya metric, we associate radar scatterers to real-world objects. Interpreting deformation of individual infrastructure is shown to be feasible for this type of medium-resolution data.
Persistent scatterers (PSs) are coherent measure-1 ment points obtained from time series of satellite radar images, 2 which are used to detect and estimate millimeter-scale displace-3 ments of the terrain or man-made structures. However, asso-4 ciating these measurement points with specific physical objects 5 is not straightforward, which hampers the exploitation of the 6 full potential of the data. We have investigated the potential 7 for predicting the occurrence and location of PSs using generic 8 3-D city models and ray-tracing methods, and proposed a 9 methodology to match PSs to the pointlike scatterers predicted 10 using RaySAR, a ray-tracing synthetic aperture radar simulator. 11 We also investigate the impact of the level of detail (LOD) of the 12 city models. For our test area in Rotterdam, we find that 10% 13 and 37% of the PSs detected in a stack of TerraSAR-X data 14 can be matched with point scatterers identified by ray tracing 15 using LOD1 and LOD2 models, respectively. In the LOD1 case, 16 most matched scatterers are at street level while LOD2 allows 17 the identification of many scatterers on the buildings. Over 18 half of the identified scatterers easily correspond to identify 19 double or triple-bounce scatterers. However, a significant fraction 20 corresponds to higher bounce levels, with approximately 25% 21 being fivefold-bounce scatterers. 22 Index Terms-Level of detail (LOD), persistent scatterers 23 (PSs), ray tracing, simulation, synthetic aperture radar (SAR). 24 I. INTRODUCTION 25 P ERSISTENT scatterer (PS) interferometry (PSI) [1] is 26 a geodetic technique to measure surface displacements 27 using multiepoch synthetic aperture radar (SAR) images.
Soil deformation is believed to play a crucial role in the onset of failures in the underground infrastructure. This article describes a method to generate a replacement-prioritisation map for underground drinking water pipe networks using ground movement data. A segment of the distribution network of a Dutch drinking water company was selected as the study area. Failure registration data comprising 868 failures registered over 40 months and geographical network data were obtained from the water utility. Ground movement was estimated using radar satellite data. Two types of analyses were performed: cell and pixel based. For the cell-based analysis, asbestos cement (AC) pipes exhibited the highest failure rates. Older AC pipes were also shown to fail more often, whereas failure rates for PVC were the lowest. For the pixel-based analysis, ground movement was demonstrated to play a role in the failure of all materials combined. Therefore, a replacement-prioritisation map for AC was generated which combined ground movement data and pipe-age data. This method can be a beneficial resource for network managers for maintenance and continuous monitoring.
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