Hierarchical Bayesian nets for building extraction using dense digital surface models

Abstract During the last years an increasing demand for 3D data of urban scenes can be recognized. Techniques for automatic acquisition of buildings are needed to satisfy this demand in an economic way. This paper describes an approach for building extraction using digital surface models (DSM) as input data. The first task is the detection of areas within the DSM which describe buildings. The second task is the reconstruction of geometric building descriptions. In this paper we focus on new extensions of our approach. The first extension is the detection of buildings using two alternative classification schemes: a binary or a statistical classification based on Bayesian nets, both using local geometric properties. The second extension is the extraction of roof structures as a first step towards the reconstruction of polyhedral building descriptions.

[1]  Wolfgang Förstner,et al.  A hybrid concept for 3D building acquisition , 1998 .

[2]  Anil K. Jain,et al.  Segmentation and Classification of Range Images , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  E. Baltsavias,et al.  Automatic Extraction of Man-Made Objects from Aerial and Space Images (II) , 1995 .

[4]  M. Nagao,et al.  Edge preserving smoothing , 1979 .

[5]  Wolfgang Eckstein,et al.  Extracting objects from digital terrain models , 1995, Optics & Photonics.

[6]  Uwe Weidner,et al.  An Approach to Building Extraction from Digital Surface Models , 1996 .

[7]  Ralf Reulke,et al.  Comparison of image reconstruction algorithms, Remote Sensing and Reconstruction for three-dimensional objects and scenes , 1995 .

[8]  Allen R. Hanson,et al.  Building reconstruction from optical and range images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  E. Baltsavias,et al.  Use of DTMs/DSMs and orthoimages to support building extraction , 1995 .

[10]  Wolfgang Förstner,et al.  Model-Based 2D-Shape Recovery , 1995, DAGM-Symposium.

[11]  Jake K. Aggarwal,et al.  Model-based object recognition in dense-range images—a review , 1993, CSUR.

[12]  Eberhard Gülch.  Application of Semi-Automatic Building Acquisition , 1997 .

[13]  Wolfgang Förstner,et al.  Towards automatic building extraction from high-resolution digital elevation models , 1995 .

[14]  Claus Brenner,et al.  Interpretation of Urban Surface Models Using 2D Building Information , 1998, Comput. Vis. Image Underst..

[15]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Christoph Hug,et al.  Extracting Artificial Surface Objects from Airborne Laser Scanner Data , 1997 .

[17]  Uwe Weidner,et al.  Gebäudeerfassung aus digitalen Oberflächenmodellen , 1997 .

[18]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.