3D building reconstruction from spaceborne TomoSAR point cloud

Modern synthetic aperture radar satellites (e.g. TerraSAR X/TanDEM X and CosmoSky Med) provides meter resolution data which when processed using advanced interferometric techniques, such as SAR tomography (or TomoSAR) enables generation of 3 D (or even 4 D) point clouds with point density of around 1 million points/km^2. Taking into consideration special characteristics associated to these point clouds e.g. low positioning accuracy (in the order of 1m) high number of outliers, gaps in the data and rich facade information (due to the side looking geometry) the thesis aims to explore for the first time the potential of explicitly modelling the individual roof surfaces to reconstruct 3 D prismatic building models from space. The developed approach is completely data driven and except for vertical facades assumption, it does not impose any constraint on the shape of building footprint (or to its constituent roof segments) i.e. any arbitrarily shaped building could be reconstructed in 3 D with several roof layers. The workflow is modular and consists of following three modules: Preprocessing and normalized DSM generation (Extraction of building regions) First a conventionally used ground filtering procedure is adopted to extract ground points from which a digital terrain model (DTM) is generated. Then among non ground points, first the data gaps are filled using the contextual facade information and later digital surface model (DSM) is generated via nearest neighbor interpolation. Subtraction of the generated DSM with the DTM then gives us the normalized DSM (nDSM) containing the building regions/pixels which is further smoothed using BM3D (Block matching and 3 D filtering) filtering method. Segmentation of building roofs: In this module, first a gradient map is generated based on height jumps in the nDSM. Watershed segmentation is then adopted to oversegment the nDSM into different regions. Subsequently, height constrained merging is employed to refine (i.e. to reduce) the retrieved number of roof segments by taking into account the height difference of two adjacent roof segments.  Coarse outline of an individual roof segment is then reconstructed using alpha shapes algorithm. Due to varying and lower point density of TomoSAR points, alpha shapes however only define the coarse outline of an individual building which is usually rough and therefore needs to be refined/smoothed (or generalized) To this end, taking into account the average roof polygon complexity (APC) a regularization scheme based on either model fitting (i.e. minimum bounding ellipse/rectangle) or quadtree is adopted to simplify the roof polygons obtained around each segmented (or distinct) roof segment. The simplified roof polygons are then tested for zig zag line removal using Visvalingam Whyatt algorithm. Finally, height is associated to each regularized roof segment to obtain the 3 D prismatic model of individual buildings. The proposed approach is illustrated and validated over scenes containing two large buildings in the city of Las Vegas using TomoSAR point clouds generated from a stack of 25 images using Tomo GENESIS software developed at German Aerospace Center (DLR) Apart from the above mentioned processing scheme, a complimentary workflow that works directly over unstructured TomoSAR point clouds (i.e. without rasterization to DSM) has also been developed as part of this thesis. The workflow adopts a typical processing chain as employed using conventional airborne laser scanning point clouds and is comprised of RANSAC based recursive plane fitting and computation of adjacent planar intersections. In addition to this, preliminary ideas towards possible future improvements, e.g. joint exploitation of amplitude/intensity together with the 3 D spatial information of each point, aiming to increase the accuracy of reconstructed models from TomoSAR point clouds are also introduced and discussed in this thesis.