Conference Agenda

This study investigates the suitability of Distributed Scatterers (DS) for satellite-based bridge monitoring in very high-resolution (VHR) Synthetic Aperture Radar (SAR) data. While Persistent Scatterer Interferometry (PSI) relies on isolated, temporally stable reflectors, the DS concept extends the analysis to statistically homogeneous areas. In bridge monitoring, however, elevated and narrow structures challenge the assumption of spatial homogeneity due to signal contributions from both the bridge deck and the underlying terrain in side-looking SAR geometry.

Using 23 TerraSAR-X Staring Spotlight acquisitions (September 2022 - September 2023) over two highway bridges near Regensburg, Germany, the study analyses the effects of layover and partial pixel mixing on height correction and deformation estimation. The DS identification is based on statistical homogeneity testing and covariance estimation, with coherence thresholds applied to ensure phase stability.

Results demonstrate that bridge decks exhibit variable coherence depending on surface roughness and illumination geometry. In some cases, overlayed signals from bridge and ground surfaces produce erroneous elevation and deformation values. The analysis highlights the need for careful interpretation of DS results in VHR data and provides insights into the limitations and potential of DS-based InSAR for linear infrastructure monitoring.

Taipei has experienced an important urban development in the recent years with the expansion of its Taipei Mass Rapid system (MRT). This expansion is currently taking place in the Tamsui-Xinyi Line (Red Line) with one new metro station, the Guangci Fengtian Temple Station. This station connects the east part of the Xinyi district as the continuation of the Xiangshan Station. This project extension has been claimed to be one of the most difficult ones in the metro line development due to its complex geological setting going from very soft sediments to hard rock in a few meters. We have employed Sentinel-1 SAR images to measure the tunnel excavation settlement utilizing ascending and descending tracks and estimating vertical and horizontal time series deformations.

Structural health monitoring is essential for building safety. While SAR provides all-weather, non-contact imaging, it is often affected by geometric distortions like layover and foreshortening, making it difficult to extract accurate 3D structural information from complex targets like buildings. Inspired by stereo vision, we propose a stereo SAR mode that acquires two images via a single rotation. By transforming Cartesian to polar coordinates, the disparity is constrained to the angular direction, significantly simplifying the matching process. We derive the nonlinear relationship between height and disparity and apply Newton’s iterative method for accurate 3D reconstruction. Real data collected by a millimetre-wave radar system validate the effectiveness of the proposed approach.

3D city models have become an important piece of geoinformation. They are available in different Levels of Detail (LoD), which determine the amount of complexity provided in the model. LoD1 city models represent simple prismatic building volumes and are typically produced by means of remote sensing. In this article, we investigate the possibility for country-wide reconstruction of LoD1 city models from TanDEM-X intensity images by utilizing deep learning-based single-image height and building footprint reconstruction. As study area, we use the land surface of the country of Denmark. Our results show the general potential of this AI-based approach of country-wide city model reconstruction, which can serve as a data-efficient pipeline that is particularly well-suited in time-critical scenarios or for the exploitation of archive imagery of satellite missions with global data coverage.

Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure millimetre-level deformation of bridges and other struc-tures. However, retrieving multi-dimensional displacements of a bridge and integrating these measurements with structural stress for coupled analysis remains a major challenge. To tackle this issue, we propose an integrated framework and demonstrate its application on the Hutiaohe extra-large bridge in Guizhou Province. First, a two-dimensional E-PS-InSAR time-series processing chain is de-veloped to derive the bridge’s bi-directional deformation. Next, structural temperatures are obtained through the ANUSPLIN interpo-lation scheme, allowing the accurate isolation of the thermal response. Finally, the finite-element model (FEM) of the bridge is con-structed to interpret the observed deformation and thermal signatures within the structural context. The results show that, compared to conventional InSAR approaches, the proposed framework yields a richer set of insights by conducting a joint analysis mul-ti-dimensional deformation, structural behavior and thermal effects.

Monitoring linear infrastructures such as railways and highways with Multitemporal Interferometric Synthetic Aperture Radar (MTInSAR) requires to identify spatial displacement gradients to assess and mitigate the related hazard. During conventional MTInSAR, the majority of the processed pixels are not directly relevant to the linear infrastructure. However, these pixels are required to aid the phase unwrapping and to remove the atmospheric phase contribution. To overcome this limitation, we propose a new method that directly estimates the spatial gradient from the Synthetic Aperture Radar (SAR) images solely along the linear infrastructure avoiding costly phase unwrapping, error propagation from pixels outside the linear infrastructure and atmospheric filtering. Our experiments based on high and medium resolution images from TerraSAR-X and Sentinel-1, respectively, demonstrate that the estimated spatial gradients agree well with the MTInSAR results with a maximum Root Mean Square Error (RMSE) of 3.5 mm/year. Applying our method on Sentinel-1 images enables computationally efficient monitoring of linear infrastructures exploiting the wide area coverage and availability of the SAR images.