Automatic extraction of building statistics from digital orthophotos

In this paper we discuss uses of image segmentation, feature extraction and Bayesian networks for identifying buildings in digital orthophotos and the utilisation of the results for the automated computation of building statistics. Our work differs from previous attempts in a number of ways. Firstly, image segmentation is accomplished using an adaptive multi-scale method which brings together region and edge information to segment the image into regions. Secondly, automated building feature extraction (e.g. corners) is optimised to fit with expert human annotation performance. The third aspect of this work is the exploration of Bayesian networks as a method for fusing available information (ranging from corner information to solar angles as indicators of shadow location) to classify segmented regions as corresponding to buildings or not. Such processes then permit the automatic compilation of building statistics.

[1]  SiteCity: a semi-automated site modelling system , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Josef Kittler,et al.  Application of a Bayesian Network in a GIS Based Decision Making System , 1998, Int. J. Geogr. Inf. Sci..

[3]  D. Yogi Goswami,et al.  Principles of Solar Engineering , 1978 .

[4]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[5]  Ramakant Nevatia,et al.  Detection and description of buildings from multiple aerial images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Charles A. Bouman,et al.  Multiple Resolution Segmentation of Textured Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Josef Kittler,et al.  Bayesian and neural networks for geographic information processing , 1996, Pattern Recognit. Lett..

[8]  Michel Roux,et al.  Feature matching for building extraction from multiple views , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[10]  J. Morel,et al.  A multiscale algorithm for image segmentation by variational method , 1994 .

[11]  David J. Spiegelhalter,et al.  Sequential updating of conditional probabilities on directed graphical structures , 1990, Networks.

[12]  David M. McKeown,et al.  Toward Automatic Cartographic Feature Extraction , 1990 .

[13]  Helmut Mayer,et al.  Automatic Object Extraction from Aerial Imagery - A Survey Focusing on Buildings , 1999, Comput. Vis. Image Underst..

[14]  Richard E. Neapolitan,et al.  Cognitive Processing of Causal Knowledge , 1997, UAI.

[15]  Brendan McCane,et al.  Multi-scale adaptive segmentation using edge and region based attributes , 1997, Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97.

[16]  Stuart J. Russell,et al.  Local Learning in Probabilistic Networks with Hidden Variables , 1995, IJCAI.

[17]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[18]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[20]  Max A. Viergever,et al.  Kohonen networks for multiscale image segmentation , 1994, Image Vis. Comput..

[21]  Richard E. Neapolitan,et al.  Probabilistic reasoning in expert systems - theory and algorithms , 2012 .

[22]  Alan Liu,et al.  Multiscale Medial Analysis of Medical Images , 1993, IPMI.

[23]  Philipp Nadel,et al.  Mapping and Spatial Modelling for Navigation , 1990, NATO ASI Series.

[24]  Alan Liu,et al.  MuItiscale medial analysis of medical images , 1994, Image Vis. Comput..

[25]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[26]  Steen Andreassen,et al.  A munin network for the median nerve - a case study on loops , 1989, Appl. Artif. Intell..

[27]  John W. Crawford,et al.  An application of belief networks to future crop production , 1994, Proceedings of the Tenth Conference on Artificial Intelligence for Applications.

[28]  Michael Spann,et al.  Adaptive segmentation of noisy and textured images , 1994, Pattern Recognit..

[29]  Harvey J. Miller,et al.  GIS and Geometric Representation in Facility Location Problems , 1996, Int. J. Geogr. Inf. Sci..

[30]  Bruce Abramson.  The design of belief network-based systems for price forecasting , 1994 .

[31]  P. Bartels,et al.  Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast. , 1994, Journal of clinical pathology.

[32]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[33]  Allen R. Hanson,et al.  The UMass Ascender System for 3 D Site Model Construction * , 1996 .