Automatic Object Extraction from Aerial Imagery – A Survey Focusing on Buildings Helmut Mayer Chair for Photogrammetry and Remote Sensing, <s> Complexity of Data <s> We present a surface texture and microstructure extraction system to provide added realism in visualization and virtual reality applications. The system uses multiple images, 3-D models and camera information, in addition to knowledge about man-made structures, to cope with problems such as perspective distortion, data deficiency, and corruption caused by shadows and occlusions. Combined with the Ascender site modeling system and scene rendering algorithms, the system is typically useful for urban site model refinement and visualization. <s> BIB001 </s> Automatic Object Extraction from Aerial Imagery – A Survey Focusing on Buildings Helmut Mayer Chair for Photogrammetry and Remote Sensing, <s> Complexity of Data <s> This paper addresses two major issues: 3-D building reconstruction and the role of color attributes and similarity grouping. We present ARUBA, a general framework for automated 3-D building reconstruction from multiple color aerial images. After highlighting the strategy and concisely describing the framework and its 2-D and 3-D processing modules, we will evaluate the reconstructed roofs with respect to accurate reference data. The second part of the paper shows that geometry, although important, should not be the only source of information exploited in the reconstruction process. The main objectives are to demonstrate that (1) color is a very important cue in reconstructing a general class of objects, (2) it is crucial to retain all information during the entire processing chain, (3) a general class of objects parts can be efficiently extracted by grouping edges and lines by means of similarity, and (4) a mutual interaction between 2-D and 3-D processing is important. <s> BIB002
Herman and Kanade's approach  was selected as it is an often cited early approach for the extraction of buildings which uses AI-focused 3D-reasoning in combination with heuristics about the vertical and horizontal directions of lines to extract buildings as rectangular prisms. Some years later buildings still were modeled as rectangular prisms, but the 3D-structure was generated by matching higher level structures such as rectangles in stereo images found by grouping rectangular or parallel edges  . What can be done in single, possibly oblique images to extract flat and peaked roof buildings using shadows and visible vertical edges based on vanishing points is exemplified by  . Wang et al. BIB001 demonstrate that the semantics of buildings is not restricted to the geometry. From several images optimally rectified images of walls are calculated and used to extract part structures of windows and doors in the walls which can help to raise the probability of the extraction. That prismatic as well as parameterized buildings, like peaked ones, can be Automatic Object Extraction from Aerial Imagery 10 detected and extracted from a Digital Surface Model (DSM) is shown by  . Some of the most recent and sophisticated approaches match primitives such as edges or corners in several images based on a detailed image model, and a complex strategy as presented by  and BIB002 . Whereas BIB002 only tries to find parts of the roof, in  a generic model for buildings was elaborated which consists of a combination of parameterized building parts. Table 3 gives the complexity of data an approach can handle split according to Subsection 2.3 into resolution and content. It can be seen that the most recent approaches are the ones which can handle the most complicated scenes.