Abstract
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.
Highlights
Three dimensional (3-D) building models reconstruction in urban areas has been a hot topic in remote sensing, photogrammetry, compute vision for more than two decades (Gruen et al, 1997, Rottensteiner et al, 2012). 3-D city models are used widely for urban planning, change detection, commercial and public sector simulations for environmental research, location based services and many others (Verma et al, 2006, Brenner, 2005, Rau and Lin, 2011)
Taking into consideration special characteristics associated to these point clouds e.g., low positioning accuracy, high number of outliers, gaps in the data and rich facade information, this paper demonstrates for the first time the potential of explicitly modelling the individual roof surfaces to reconstruct 3-D prismatic building models from tomographic synthetic aperture radar (TomoSAR) point clouds
The proposed approach is illustrated and validated over a large building in the city of Las Vegas using TomoSAR point clouds generated from a stack of 25 images using Tomo-GENESIS software developed at DLR (Zhu, 2011, Zhu et al, 2013)
Summary
Three dimensional (3-D) building models reconstruction in urban areas has been a hot topic in remote sensing, photogrammetry, compute vision for more than two decades (Gruen et al, 1997, Rottensteiner et al, 2012). 3-D city models are used widely for urban planning, change detection, commercial and public sector simulations for environmental research, location based services and many others (Verma et al, 2006, Brenner, 2005, Rau and Lin, 2011). Numerous research papers have been published in this context that provides different reconstruction methods using variety of data sources. Recent advances in very high resolution synthetic aperture radar imaging together with its key attributes such as self-illumination and all-weather capability, have attracted attention of many remote sensing analysts in characterizing urban objects such as buildings. In order to fully localize a point in 3-D, advanced interferometric SAR (InSAR) techniques are required that process stack(s) of complex-valued SAR images to retrieve the lost third dimension (i.e., the “elevation” coordinate). By exploiting stack(s) of SAR images taken from slightly different positions, it builds up a synthetic aperture in elevation that enables the retrieval of precise 3-D position of dominant scatterers within one azimuth-range SAR image pixel. TomoSAR processing of very high resolution data of urban areas provided by modern satellites
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