Accurate and Automatic Building Roof Extraction Using Neighborhood Information of Point Clouds

doi:10.11947/j.AGCS.2017.20160518

Abstract

Abstract: High accuracy building roof extraction from LiDAR data is the key to build topological relationship of building roofs and reconstruct buildings. Aiming at the poor adaptation and low extraction precision of existing roof extraction methods for complex building, an accurate and automatic building roof extraction method using neighborhood information of point clouds is proposed. Point clouds features are calculated by principle component analysis, and reliable seed points are selected after feature histogram construction. Initial roof surfaces are extracted quickly and precisely by the proposed local normal vector distribution density-based spatial clustering of applications with noise (LNVD-DBSCAN). Roof competition problem is solved effectively by the poll model based on neighborhood information. Experimental results show that the proposed method can extract building roofs automatically and precisely, and has preferable adaptation to buildings with different complexity, which is able to provide reliable roof information for building reconstruction.

Key words: building roofs, LiDAR data, neighborhood information, density-based clustering, point cloud, 3D building reconstruction

CLC Number:

P237

ZHAO Chuan, ZHANG Baoming, CHEN Xiaowei, GUO Haitao, LU Jun. Accurate and Automatic Building Roof Extraction Using Neighborhood Information of Point Clouds[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(9): 1123-1134.

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