Robust Building Facade Reconstruction from Spaceborne TOMOSAR Points
- 1Chair of Remote Sensing Technology (LMF), Technical University Munich, Germany, Arcisstrasse 21, 80333 Munich, Germany
- 2Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Weßling, Germany
Keywords: SAR tomography, TerraSAR-X, façade reconstruction, 4D point cloud, clustering, segmentation
Abstract. With improved sensor resolution and advanced multi-pass interferometric techniques such as SAR tomographic inversion (TomoSAR), it is now possible to reconstruct both shape and motion of urban infrastructures. These sophisticated techniques not only opens up new possibilities to monitor and visualize the dynamics of urban infrastructure in very high level of details but also allows us to take a step further towards generation of 4D (space-time) or even higher dimensional dynamic city models that can potentially incorporate temporal (motion) behaviour along with the 3D information. Motivated by these chances, this paper presents a post processing approach that systematically allows automatic reconstruction of building façades from 4D point cloud generated from tomographic SAR processing and put the particular focus on robust reconstruction of large areas. The approach is modular and consists of extracting facade points via point density estimation procedure based on directional window approach. Segmentation of facades into individual segments is then carried out using an unsupervised clustering procedure combining both the density-based clustering and the mean-shift algorithm. Subsequently, points of individual facade segments are identified as belonging to flat or curved surface and general 1st and 2nd order polynomials are used to model the facade geometry. Finally, intersection points of the adjacent façades describing the vertex points are determined to complete the reconstruction process. The proposed approach is illustrated and validated by examples using TomoSAR point clouds over the city of Las Vegas generated from a stack of TerraSARX high resolution spotlight images.