Abstract
This paper highlights the uses of Centroid Neural Network for detecting and reconstructing the 3D rooftop model from aerial image data. High overlapping aerial images are used as an input to the method. The Digital Elevation Map ( DEM) data and 2D lines are generated and then combined to form 3D lines. The core of the technique is a clustering process using Centroid Neural Network algorithm to classify these 3D lines into groups of lines that belong to the corresponding building areas. This work differs from the previous researches, as it affiliates 3D lines and corners - obtained by applying the Harris corner detector - to automatically extract accurate and reliable 3D rooftop information. The proposed approach is tested with the synthetic images generated from the Avenches dataset of the Ascona aerial images and gives an average error of 0.38m in comparison with the ground truth data. The experiment result proves the applicability and efficiency of the method in dealing with building reconstruction in complicated scenes.
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References
Collins, R.T., Jaynes, C.O., Cheng, Y.Q., Wang, X., Stolle, F., Riseman, E.M., Hanson, A.R.: The Ascender system: automated site modeling from multiple aerial images. Computer Vision and Image Understanding (72), 143–162 (1998)
Huertas, A., Nevatia, R.: Detecting buildings in aerial images. Computer Vision, Graphics, and Image Processing 41, 131–152 (1988)
Noronha, S., Nevatia, R.: Detection and modeling of buildings from multiple aerial images. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 501–518 (2001)
Lin, C., Nevatia, R.: Building detection from a monocular image. In: Firschein, O., Strat, T.M. (eds.) RADIUS: Image Understanding for Imagery Intelligence, pp. 153–170. Morgan Kaufmann Publishers, San Francisco (1997)
Woo, D.M., Nguyen, Q.D., Park, D.C.: 3D rooftop extracting using perceptual organization based on fast graph search. In: Proceedings of the International Conference on Signal Processing, pp. 1317–1320 (2008)
Nevatia, R., Lin, C., Huertas, A.: A system for building detection from aerial images. In: Automatic Extraction of Man-Made Objects from Aerial and Space Images (1997)
Cord, M., Jordan, M., Cocquerez, J.P., Paparoditis, N.: Automatic extraction and modeling of urban buildings from high resolution aerial images. In: IAPRS 1999 (September 1999)
Fischer, A., Kolbe, T.H., Lang, F.: Integration of 2D and 3D Reasoning for Building Reconstruction Using a Generic Hierarchical Model. In: Forstner, W. (ed.) Proc. Workshop Semantic Modeling for the Acquisition of Topographic Information, Bonn, Germany (1997)
Harris, C., Stephens, M.J.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–152 (1988)
Park, D.C.: Centroid neural network for unsupervised competitive learning. IEEE Trans. on Neural Network 11, 520–528 (2000)
Perona, P., Freeman, W.: A factorization approach to grouping. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 655–670. Springer, Heidelberg (1998)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)
Woo, D.M., Han, S.S., Park, D.C., Nguyen, Q.D.: Extraction of 3D line segment using digital elevation data. In: Proceedings of the 2008 Congress on Image and Signal Processing, vol. 02, pp. 734–738 (2008)
Canny, J.: A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Boldt, M., Weiss, R., Riseman, E.M.: Token-based extraction of straight lines. IEEE Trans. Systems, Man and Cybernectics 19, 1581–1594 (1989)
Nacken, P.F.M.: A metric for line segments. IEEE Trans. on pattern analysis and machine intelligence 15, 1312–1318 (1993)
Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)
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Woo, DM., Park, DC., Ho, HN. (2010). Applying an Artificial Neural Network to Building Reconstruction. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_42
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DOI: https://doi.org/10.1007/978-3-642-16493-4_42
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