Geocoding of ground-based SAR data for infrastructure objects using the Maximum A Posteriori estimation and ray-tracing

Abstract

Ground-Based Synthetic Aperture Radar (GB-SAR) enables to monitor a scene in a distance up to a few km and with an accuracy of around one mm. It is mainly used for landslide monitoring but also used to monitor man-made structures such as bridges and dams. Due to the particular distance-dependent imaging concept and related geometric distortion effects, the interpretation of the SAR images can be difficult, especially in the case of complex man-made structures. The geocoding of the SAR data enables to project the 2D SAR image on a 3D digital elevation model (DEM) or a 3D terrain model and highly improves the interpretation of the observed deformation. It also allows to fuse SAR-based observations with other data sources. The purpose of this paper is to present a new algorithm to geocode GB-SAR data in complex scenarios. The method introduces two new components, a Bayesian statistical approach and a ray-tracing algorithm. The first one uses an a priori function on the received intensity to improve the geocoding in case of strong layover, while the latter considers areas in the radar shadow to be excluded from geocoding. Moreover, ray-tracing provides the starting point for the analysis of signals with multiple reflections which are also discussed in this paper. Our algorithm is applied to and evaluated with two case studies. The first one demonstrates the improved geocoding of GB-SAR data on the Linach dam in the Black Forest in Germany, which is characterized by a rather complex architecture. The second case study shows the application of the method at the Enguri dam in Georgia, which is characterized by its large dimensions and a dam height over 270m.