Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 381-386, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
09 Jun 2016
Y. Sun1,3, M. Shahzad1, and X. Zhu1,2 1Signal Processing in Earth Observation (SiPEO), Technical University of Munich, 80333 Munich, Germany
2Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Weßling, Germany
3School of Resource and Environmental Science, Wuhan University, 430079, Wuhan, China
Keywords: TerraSAR-X, tomographic SAR inversion (TomoSAR), 4D point clouds, building reconstruction, watershed segmentation Abstract. This paper demonstrates for the first time the potential of explicitly modelling the individual roof surfaces to reconstruct 3-D prismatic building models using spaceborne tomographic synthetic aperture radar (TomoSAR) point clouds. The proposed approach is modular and works as follows: it first extracts the buildings via DSM generation and cutting-off the ground terrain. The DSM is smoothed using BM3D denoising method proposed in (Dabov et al., 2007) and a gradient map of the smoothed DSM is generated based on height jumps. Watershed segmentation is then adopted to oversegment the DSM into different regions. Subsequently, height and polygon complexity constrained merging is employed to refine (i.e., to reduce) the retrieved number of roof segments. Coarse outline of each roof segment is then reconstructed and later refined using quadtree based regularization plus zig-zag line simplification scheme. Finally, height is associated to each refined roof segment to obtain the 3-D prismatic model of the building. The proposed approach is illustrated and validated over a large building (convention center) in the city of Las Vegas using TomoSAR point clouds generated from a stack of 25 images using Tomo-GENESIS software developed at DLR.
Conference paper (PDF, 1058 KB)

Citation: Sun, Y., Shahzad, M., and Zhu, X.: FIRST PRISMATIC BUILDING MODEL RECONSTRUCTION FROM TOMOSAR POINT CLOUDS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 381-386,, 2016.

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