基于改进空间密度聚类的电力线自动提取  

Automatic Extraction of Power Lines Based on Improved Spatial Density Clustering

作　　者：齐智宇 王健[1,2] 赵艺龙 李志远 Qi Zhiyu;Wang Jian;Zhao Yilong;Li Zhiyuan(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,Shandong,China;Qingdao Key Laboratory of Beidou Navigation and Intelligent Spatial Information Technology Application,Qingdao 266590,Shandong,China;Operation Management Department of Songxian Shanjin Mining Co.,Ltd,Luoyang 471400,Henan,China)

机构地区：[1]山东科技大学测绘与空间信息学院,山东青岛266590 [2]青岛市北斗导航空间信息技术重点实验室,山东青岛266590 [3]嵩县山金矿业有限公司运营管理部,河南洛阳471400

出　　处：《应用激光》2025年第2期141-147,共7页Applied Laser

基　　金：“十四五”国家重点研发计划项目(2022YFB3903501);山东省自然科学基金(ZR2023MD027)。

摘　　要：针对电力线点云提取过程中自动化程度低且结果易受参数影响出现欠分割或过分割的问题,结合机载激光雷达(light detection and ranging,LiDAR)点云数据的分布特点,提出一种基于改进空间密度聚类算法的激光点云电力线的提取方法。该方法首先通过空间分割改进高程滤波算法完成电力线点云的粗提取;其次,利用基于距离-密度的方法和数学期望计算方法获得空间密度聚类的最佳参数,避免了繁杂的人工调参过程。实验结果显示,相较于空间密度聚类算法,所提算法效率显著提高,降低了约60%电力线提取时间,实现了单根电力线点云的自动化和高效提取。To address the issues of low automation and segmentation errors caused by parameters in power line point cloud extraction,this paper proposes a power line extraction method based on an improved spatial density clustering algorithm,combined with the distribution characteristics of airborne LiDAR point cloud data.Firstly,the proposed method completed the rough extraction of power line point cloud through the improved elevation filtering algorithm.Then,the optimal parameters of spatial density clustering were obtained by the distance-density method and the mathematical expectation calculation method,avoiding the complicated manual parameter adjustment process.Experimental results show that compared with the spatial density clustering algorithm,the proposed algorithm has significantly improved efficiency,reduced the power line extraction time by about 60%,and realized the automatic and efficient extraction of single power line point cloud.

关 键 词：机载激光雷达 高程滤波 点云数据 空间密度聚类 电力线提取 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]