A Method of Extracting Building Based on LiDAR Point Clouds

doi:10.13474/j.cnki.11-2246.2017.0044

Abstract

Abstract:

Extracting buildings from LiDAR cloud points quickly and precisely is a difficult and hot spot in current research. On the basis of analysing existing building extraction methods sufficiently, a building extraction method is proposed based on LiDAR point cloud. Firstly, initial building contour points are extracted according to geometric characteristics of buildings. To eliminate false building contour points, distribution of LiDAR point cloud is then calculated by constructing the local covariance matrix. Finally, buffer zones are built based on clustering result of building contour points by using DBSCAN clustering algorithm, all building contour points in each buffer zone are selected as initial seed points, and multi-seed points region growing process, adopting a cylinder neighborhood system, is applied to achieve extracting buildings quickly and accurately. Two sets of experiments, concluding a total of five datasets, have been realized to verify all aspects performance of the algorithm. Experimental results show that the method can extract multistorey and complex buildings accurately and effectively, and performs high efficiency and strong applicability.

Key words: building extraction, building contour points, multi-seed points, region growing, LiDAR point cloud

CLC Number:

P237

ZHAO Chuan, ZHANG Baoming, CHEN Xiaowei, GUO Haitao, TANG Liangke. A Method of Extracting Building Based on LiDAR Point Clouds[J]. 测绘通报, 2017, 0(2): 35-39.

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