Segmentation of buildings based on high resolution persistent scatterer point clouds

Abstract

Integrating differential synthetic aperture radar measurements into building information modeling systems requires a mapping of these measurement points onto structural parts of the building. We use a reverse geocoding approach to project building footprints into slant-range geometry, which helps to accurately assign PS points to single building identities. By treating the deformation time series as points in a high dimensional feature space, we can use dimensional reduction and clustering techniques to extract clusters of points that show a similar movement behavior. We visualize these clusters by mapping them onto ground truth, using laser scanning point clouds. Our approach segments buildings into plausible parts.