3D building modeling and reconstruction using photometric satellite and aerial imageries

In this thesis, the problem of three dimensional (3D) reconstruction of building models using photometric satellite and aerial images is investigated. Here, two systems are presented: 1) 3D building reconstruction using a nadir single-view image, and 2) 3D building reconstruction using slant multiple-view aerial images. The first system detects building rooftops in orthogonal aerial/satellite images using a hierarchical segmentation algorithm and a shadow verification approach. The heights of detected buildings are then estimated using a fuzzy rule-based method, which measures the height of a building by comparing its predicted shadow region with the actual shadow evidence in the image. This system finally generated a KML (Keyhole Markup Language) file as the output, that contains 3D models of detected buildings. The second system uses the geolocation information of a scene containing a building of interest and uploads all slant-view images that contain this scene from an input image dataset. These images are then searched automatically to choose image pairs with different views of the scene (north, east, south and west) based on the geolocation and auxiliary data accompanying the input data (metadata that describes the acquisition parameters at the capture time). The camera parameters corresponding to these images are refined using a novel point matching algorithm. Next, the system independently reconstructs 3D flat surfaces that are visible in each view using an iterative algorithm. 3D surfaces generated for all views are combined, and redundant surfaces are removed to create a complete set of 3D surfaces. Finally, the combined 3D surfaces are connected together to generate a more complete 3D model. For the experimental results, both presented systems are evaluated quantitatively and qualitatively and different aspects of the two systems including accuracy, stability, and execution time are discussed.