Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 291-296, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
31 Jul 2012
C. Liu2,1, B. Shi3,1, X. Yang1, and N. Li1 1Department of Survey and Geo-Informatics, Tongji University, Shanghai, China
2Key Laboratory of Advanced Engineerig Surveying of NASMG, Shanghai, China
3Urban Information Research Center, Shanghai Normal University, Shanghai, China
Keywords: LiDAR, DSM, LEGION segmentation, Building Extraction, Height Texture Abstract. Recently, a neural oscillator network based on biologically framework named LEGION (Locally Excitatory Globally Inhibitory Oscillator Network), which each oscillator has excitatory lateral connections to the oscillators in its local neighbourhood as well as a connection with a global inhibitor, has been applied to segmentation field. The extended LEGION approach is constructed to extract buildings digital surface model (DSM) generated from LiDAR data. This approach is with no assumption about the underlying structures in DSM data and no prior knowledge regarding the number of regions. Instead of using lateral potential to find a major oscillator block in original way, Gray Level Co-occurence Matrix (GLCM) homogeneity measuring DSM height texture is applied to distinguish buildings from trees and assist to find LEGION leaders in building targets. Alongside the DSM height texture attribure, extended LEGION can extract buildings close to trees automatically. Then a solution of least squares with perpendicularity constraints is put forward to determine regularized rectilinear building boundaries, after tracing and connection the rough building boundaries. In general, the paper presents the concept, algorithms and procedures of the approach. It also gives experimental result of Vaihingen A2 region by the ISPRS text project and another result based on a DSM data of suburban area. The experiment result showed that the proposed method can effectively produce more accurate buildings boundary extraction.
Conference paper (PDF, 794 KB)

Citation: Liu, C., Shi, B., Yang, X., and Li, N.: LEGION SEMENTATION FOR BUILDING EXTRACTION FROM LIDAR BASED DSM DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 291-296,, 2012.

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