Abstract
Abstract. This paper presents an approach that automatically (but parametrically) reconstructs 2-D/3-D building footprints using 3-D synthetic aperture radar (SAR) tomography (TomoSAR) point clouds. These point clouds are generated by processing SAR image stacks via SAR tomographic inversion. The proposed approach reconstructs the building outline by exploiting both the roof and façade points. Initial building footprints are derived by applying the alpha shapes method on pre-segmented point clusters of individual buildings. A recursive angular deviation based refinement is then carried out to obtain refined/smoothed 2-D polygonal boundaries. A robust fusion framework then fuses the information pertaining to building façades to the smoothed polygons. Afterwards, a rectilinear building identification procedure is adopted and constraints are added to yield geometrically correct and visually aesthetic building shapes. The proposed approach is illustrated and validated using TomoSAR point clouds generated from a stack of TerraSAR-X high-resolution spotlight images from ascending orbit covering approximately 1.5 km2 area in the city of Berlin, Germany.
Highlights
Modern very high resolution (VHR) spaceborne synthetic aperture radar (SAR) sensors such as TerraSAR-X/ TanDEM-X and COSMO-SkyMed can deliver data beyond the inherent spatial scales of buildings
We propose a novel data driven approach that systematically allows automatic reconstruction of 2-D/3-D building footprints using unstructured TomoSAR points clouds generated from one incidence angle only
We tested the algorithm on the TomoSAR point clouds generated from a stack of 102 TerraSAR-X high resolution spotlight images from ascending orbit using the Tomo-GENESIS software developed at the German Aerospace Center (DLR) (Zhu et al, 2013)
Summary
Modern very high resolution (VHR) spaceborne SAR sensors such as TerraSAR-X/ TanDEM-X and COSMO-SkyMed can deliver data beyond the inherent spatial scales of buildings. Object reconstruction from spaceborne TomoSAR point cloud has been recently started (D’Hondt et al, 2013)(Shahzad and Zhu, 2015a) (Fornaro et al, 2014) These point clouds have point density in the range of 600,000 ~ 1,000,000 points/km and are associated with some characteristics that are worth to mention (Zhu and Shahzad, 2014): 1) TomoSAR point clouds deliver moderate 3D positioning accuracy on the order of 1 m; 2) Few number of images and limited orbit spread render the location error of TomoSAR points highly anisotropic, with an elevation error typically one or two orders of magnitude higher than in range and azimuth (Zhu and Bamler, 2012); 3) Due to the coherent imaging nature, temporally incoherent objects such as trees cannot be reconstructed from multipass spaceborne SAR image stacks; 4) TomoSAR point clouds possess much higher density of points on the building façades due to side looking SAR geometry enabling systematic reconstruction of buildings footprint via façade points analysis
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