Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 41-46, 2012
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B3/41/2012/
doi:10.5194/isprsarchives-XXXIX-B3-41-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
23 Jul 2012
A REFINING METHOD FOR BUILDING OBJECT AGGREGATION AND FOOTPRINT MODELLING USING MULTI-SOURCE DATA
Y. Li1, L. Zhu2, H. Shimamura2, and K. Tachibana2 1International Institute for Earth System Science, Nanjing University, Jiangsu, China, 210093,
2Pasco corporation, 1-1-2 Higashiyama, Meguro-ku, Tokyo 153-0043
Keywords: Modelling, polygon, image, DSM, feature, fitting Abstract. Automatically detection, extraction and re-construction of 3D building modelling are difficult yet potentially high-payoff challenges for photogrammetric applications. Solution usually requires integrating various sources, including LIDAR, imagery, and digital surface models (DSM). However, highly automated and robust geometric modelling remains unsolved. We will present a 2D modelling technique which represents a building’s outline in an as-is way. It gives visually accurate corners and lines for buildings. Aerial remotely sensed imagery and a DSM are used to detect and segment building masks. A refining footprint modelling is implemented through line modelling, edge refining, and segment merging and generating. A district grouping based main orientation algorithm is proposed. This approach has the ability of successive improvement, moving from a prototype to a subtle end product. Experiments with Japanese data show that the models generated automatically fit the manual models very well.
Conference paper (PDF, 1385 KB)


Citation: Li, Y., Zhu, L., Shimamura, H., and Tachibana, K.: A REFINING METHOD FOR BUILDING OBJECT AGGREGATION AND FOOTPRINT MODELLING USING MULTI-SOURCE DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 41-46, doi:10.5194/isprsarchives-XXXIX-B3-41-2012, 2012.

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