Abstract
Abstract. In this paper, we present an approach that allows automatic (parametric) reconstruction of building shapes in 2-D/3-D using TomoSAR point clouds. These point clouds are generated by processing radar image stacks via advanced interferometric technique, called SAR tomography. The proposed approach reconstructs the building outline by exploiting both the available roof and façade information. Roof points are extracted out by employing a surface normals based region growing procedure via selected seed points while the extraction of façade points is based on thresholding the point scatterer density SD estimated by robust M-estimator. Spatial clustering is then applied to the extracted roof points in a way such that each roof cluster represents an individual building. Extracted façade points are reconstructed and afterwards incorporated to the segmented roof cluster to reconstruct the complete building shape. Initial building footprints are derived by employing alpha shapes method that are later regularized. Finally, rectilinear constraints are added to yield better geometrically looking building shapes. 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 only covering two different test areas with one containing relatively smaller buildings in densely populated regions and the other containing moderate sized buildings in the city of Las Vegas.
Highlights
With data provided by modern meter-resolution SAR sensors and advanced multi-pass interferometric techniques such as tomographic SAR inversion (TomoSAR), it is possible to generate 4-D point clouds of the illuminated area with point density of approx. 1 million points/km2
TomoSAR point clouds for both these sites are generated from a stack of 25 TerraSARX high spotlight images from ascending orbit only using the Tomo- GENESIS software developed at the German Aerospace Center (Zhu, 2013)
The building roof points extraction procedure begins by first determining the higher height regions in the input TomoSAR point clouds
Summary
With data provided by modern meter-resolution SAR sensors and advanced multi-pass interferometric techniques such as tomographic SAR inversion (TomoSAR), it is possible to generate 4-D (space-time) point clouds of the illuminated area with point density of approx. 1 million points/km. Temporally incoherent objects such as trees cannot be reconstructed from multi-pass spaceborne SAR image stacks and provide moderate 3-D positioning accuracy in the order of 1m as compared to airborne LiDAR systems (around 0.1m) Despite of these special considerations, object reconstruction from these high quality point clouds can greatly support the reconstruction of dynamic city models that could be potentially used to monitor and visualize the dynamics of urban infrastructure in very high level of details. Problems related to the visibility of façades mainly pointing towards the azimuth direction can cause difficulties in deriving the complete structure of an individual building These problems motivate us to reconstruct 2-D/3-D building shape (footprint) via roof point analysis. We propose solutions to the following two cases: 1) When only roof points are available, i.e., no or very few façade points exist; and/or
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