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Abstract: In this paper the feasibility to monitor the shape and deformation of single buildings from space is investigated. The methodology is based on a fusion of persistent scatterer (PS) point clouds obtained from several stacks of meter-resolution synthetic aperture radar (SAR) data. This kind of high resolution imagery as well as accurate orbit information is available from, e.g., TerraSAR-X. The stacks are processed individually applying persistent scatterer interferometry (PSI), which provides deformation and height estimates for the PS. However, the geocoded PS point clouds cannot be simply merged in one common coordinate system, like UTM, by reason of residual offsets with respect to their final true positions. These deviations originate from the height uncertainty of the reference point, which has to be chosen during PSI processing of each stack. The presented methodology allows for a fusion of several PS point clouds, i.e., the correct reference point heights can be recovered. The algorithm is based on a point cloud matching procedure that consists of a determination of appropriate point correspondences and a minimization of the distances between all selected pairs of points in a least-squares sense. In addition, the reconstruction of the original motion vector from the deformation measurements in line of sight provided by PSI is desirable. The availability of separated motion components in vertical and horizontal directions greatly enhances the insights into deformation events at buildings and ground. To this end, the fused point clouds are used for a decomposition of motion components. By reason of the limited sensitivity of ascending and descending stacks of TerraSAR-X to deformation in north-southern directions the reconstruction of motion vector components is restricted to components in west-eastern and vertical directions. The latter are estimated in a least-squares adjustment including all PS within a spatially limited area. Deformation estimates of stacks from ascending and descending tracks must be included in order to separate motion components. The latter cannot be determined precisely from a combination of solely equal heading tracks, as the line of sight does not differ enough. Finally, deformation maps of the urban area are available that separately show seasonal and linear deformation in horizontal as well as vertical directions. These maps comprise important information on subsidence or uplift as well as structural stress at buildings due to thermal dilation. As a result of the presented methodology (and for the first time) sufficient and precise motion estimates are available for a detailed monitoring of single objects using meter-resolution SAR data in PSI. Several examples are discussed using the results of motion component estimation based on the fusion of four data stacks evaluated by PSI. [Copyright &y& Elsevier]

Abstract: In a recent contribution Ferretti and co-workers (Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., Rucci, A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR IEEE Transactions on Geoscience and Remote Sensing 49(9), pp. 3460–3470) have proposed the SqueeSAR method, a way to exploit temporally coherent distributed scatterers in coherent SAR data stacks. Elevation and deformation or subsidence estimates are obtained with accuracy similar as in the well known persistent scatterer interferometry (PSI). In this paper we propose an alternative approach and provide a first demonstration of the optimal estimation of distributed scatterers’ phase histories in urban areas. Different to SqueeSAR, we derive phase histories for each distributed scatterer pixel rather than for groups of pixels. We use the Anderson–Darling statistical test to identify neighboring samples of the same distribution. Prior to covariance matrix estimation required for maximum likelihood estimation we apply a multi-resolution defringe technique. By using TerraSAR-X high resolution spotlight data, it is demonstrated that we are able to retrieve reliable phase histories and motion parameter estimates from distributed scatterers with signal-to-noise-ratio far below the common range. [Copyright &y& Elsevier]

Abstract: Orthorectification of satellite data is one of the most important pre-processing steps for application oriented evaluations and for image data input into Geographic Information Systems. Although high- and very high-resolution optical data can be rectified without ground control points (GCPs) using an underlying digital elevation model (DEM) to positional root mean square errors (RMSEs) between 3 m and several hundred meters (depending on the satellite), there is still need for ground control with higher precision to reach lower RMSE values for the orthoimages. The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. This is due to the fact that the SAR antenna measures distances which are mainly dependent on the terrain height and the position of the satellite. The latter can be measured with high precision, whereas the satellite attitude need not be known exactly. If the used DEM is of high accuracy, the resulting geocoded SAR data are very precise in their geolocation. This precision can be exploited to improve the orientation knowledge and thereby the geometric accuracy of the rectified optical satellite data. The challenge is to match two kinds of image data, which exhibit very different geometric and radiometric properties. Simple correlation techniques do not work and the goal is to develop a robust method which works even for urban areas, including radar shadows, layover and foreshortening effects. First the optical data have to be rectified with the available interior and exterior orientation data or using rational polynomial coefficients (RPCs). From this approximation, the technique used is the measurement of small identical areas in the optical and radar images by automatic image matching, using a newly developed adapted mutual information procedure followed by an estimation of correction terms for the exterior orientation or the RPC coefficients. The matching areas are selected randomly from a regular grid covering the whole imagery. By adjustment calculations, parameters from falsely matched areas can be eliminated and optimal improvement parameters are found. The original optical data are orthorectified again using the delivered metadata together with these corrections and the available DEM. As proof of method the orthorectified data from IKONOS and ALOS-PRISM sensors are compared with conventional ground control information from high-precision orthoimage maps of the German Cartographic Survey. The results show that this method is robust, even for urban areas. Although the resulting RMSE values are in the order of 2–6 m, the advantage is that this result can be reached even for optical sensors which do not exhibit low RMSE values without using manual GCP measurements. [Copyright &y& Elsevier]

Abstract: With TerraSAR-X, Germany will launch its first dual-channel SAR satellite mission in the year 2006. Besides other commercial applications TerraSAR-X should also be used as a demonstrator for traffic monitoring from space. This paper revises the theoretical background of traffic monitoring with space-based SARs and applies the conclusions to a performance analysis of the TerraSAR-X Traffic monitoring system. As it is well known, an object moving with a velocity deviating from the assumptions incorporated in the focusing process of the synthetic aperture radar (SAR) principle will generally appear both displaced and blurred in the image. To study the impact of these (and related) distortions in focused SAR images, the analytic relations between an arbitrarily moving point scatterer and its conjugate in the SAR image have been reviewed and adapted to the dual-channel satellite specifications of TerraSAR-X. To be able to monitor traffic under these boundary conditions in real-life situations, a specific detection scheme is proposed. This scheme integrates complementary detection and velocity estimation algorithms with knowledge derived from external sources as, e.g., road databases. The main focus of this article lies on an extensive analytical and empirical accuracy analysis for both vehicle detection and velocity estimation. The accuracy analysis includes a theoretical accuracy evaluation and a validation with real data. [Copyright &y& Elsevier]

For 11 days in February 2000, the Shuttle Radar Topography Mission (SRTM) successfully recorded by interferometric synthetic aperture radar (InSAR) data of the entire land mass of the earth between 60°N and 57°S. The data acquired in C- and X-bands are processed into the first global digital elevation models (DEMs) at 1 arc sec resolution, by NASA-JPL and German aerospace center (DLR), respectively. From the perspective of the SRTM-X system, we give in this paper an overview of the mission and the DEM production, as well as an evaluation of the DEM product quality. Special emphasis is on challenges and peculiarities of the processing that arose from the unique design of the SRTM system, which has been the first single-pass interferometer in space. [Copyright &y& Elsevier]