Automatic Object Extraction from Aerial Imagery – A Survey Focusing on Buildings Helmut Mayer Chair for Photogrammetry and Remote Sensing, <s> Characterization of Strategies <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> Characterization of Strategies <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
Many approaches fuse two (stereo) or more images (cf. Table 6 ). Additionally in  multitemporal images are used. This leads to the problem of updating for which the approach taken is relatively simple. In BIB002 colored images and a DSM  are exploited to generate hypotheses for buildings. Grouping is of significant importance and relatively complex schemes are used. It is done in the 2D image as well as in the 3D-scene and BIB002 even combines both. The control of the approaches is mostly data-driven. Only  is based on hypothesize and verify where grouping is used for the hypotheses. Wang et al.'s approach BIB001 is model-driven as the walls can only be extracted from the image using already existing information. Constraint satisfaction is used in   . If it is there at all, internal evaluation is mostly simple. Only   use a more complex evaluation based on probability theory or Minimum Description Length (MDL). In summary there is a change from simpler to more complex strategies. There is a tendency to use more than two images, but also color images or DSM. Grouping is focused on. The trend is towards complex grouping in 2D as well as in 3D. The tendency goes towards mixed strategies. Some recent approaches use the more general strategy of hypothesize and verify. For internal evaluation more and more complex modeling is used.