Robust Reconstruction of Building Façades for Large Areas Using Spaceborne TomoSAR Point Clouds

Abstract

With data provided by modern meter-resolution SAR sensors and advanced multi-pass interferometric techniques such as tomographic SAR inversion (TomoSAR), it is now possible to reconstruct the shape and monitor the undergoing motion of urban infrastructures on the scale of centimeters or even millimeters from space in very high level of details. The retrieval of rich information allows us to take a step further towards generation of 4-D (or even higher dimensional) dynamic city models, i.e. city models that can incorporate temporal (motion) behaviour along with the 3-D information. Motivated by these opportunities, the authors proposed an approach that first attempts to reconstruct façades from this class of data. The approach works well for small areas containing only a couple of buildings. Yet towards automatic reconstruction for the whole city area, a more robust and fully automatic approach is needed. In this paper, we present a complete extended approach for automatic (parametric) reconstruction of building façades from 4-D TomoSAR point cloud data and put particular focus on robust reconstruction of large areas. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from a stack of TerraSAR-X high-resolution spotlight images from ascending orbit covering approx. 2 km2 high rise area in the city of Las Vegas.