Extraction of power lines from laser point cloud based on residual clustering method

doi:10.11947/j.AGCS.2020.20190373

Abstract

Abstract: Aiming at the complex environment such as missing and noise in power line cloud data, a precise power line extraction method based on model residual clustering from LiDAR point is proposed. Firstly, the near-ground points are removed according to the normalized elevation threshold segmentation. The power line points are roughly extracted using adaptive dimension features and directional features. Secondly, the improved modeling method is adopted to determine the model residual error with the constraint condition of the parabolic model. The result obtained by density clustering on the model residual error is used to extract the single power line point. Finally, the influence of the selection of key parameters on the extraction results is discussed. Two experimental results show that the method can quickly extract power line from point cloud with partial missing and noise interference, without prior knowledge such as the number of power lines and density of point cloud, etc. Which has good applicability for different types of bundle conductor extraction. the accuracy of single power line extraction is more than 99.17%, the maximum error of model fitting is 0.167 m, and the maximum mean square error of model fitting is 0.079 m.

Key words: model residual, density clustering, point cloud data, power line extraction, model reconstruction

CLC Number:

P237

MA Weifeng, WANG Cheng, WANG Jinliang, ZHOU Jinchun, MA Yuanyuan. Extraction of power lines from laser point cloud based on residual clustering method[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 883-892.

References

[1] 周汝琴, 翟瑞聪, 江万寿, 等. 机载激光点云数据中分裂导线自动提取和重建[J]. 测绘科学, 2018, 43(6):124-130. ZHOU Ruqin, ZHAI Ruicong, JIANG Wanshou, et al. Automatic extraction and reconstruction of bundle conductors from airborne LiDAR point clouds[J]. Science of Surveying and Mapping, 2018, 43(6):124-130.
[2] 楼文娟, 吴登国, 苏杰, 等. 超高压输电线路风偏闪络及导线风荷载取值讨论[J]. 高电压技术, 2019, 45(4):1249-1255. LOU Wenjuan, WU Dengguo, SU Jie, et al. Discussion on wind-induced swing flashover and conductor wind load of EHV transmission lines[J]. High Voltage Engineering, 2019, 45(4):1249-1255.
[3] 赖旭东, 戴大昌, 郑敏, 等. LiDAR点云数据的电力线3维重建[J]. 遥感学报, 2014, 18(6):1223-1229. LAI Xudong, DAI Dacang, ZHENG Min, et al. Powerline three-dimensional reconstruction for LiDAR point cloud data[J]. Journal of Remote Sensing, 2014, 18(6):1223-1229.
[4] MILLS S J, CASTRO M P G, LI Zhengrong, et al. Evaluation of aerial remote sensing techniques for vegetation management in power-line corridors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(9):3379-3390.
[5] KOBAYASHI Y, KARADY G G, HEYDT G T, et al. The utilization of satellite images to identify trees endangering transmission lines[J]. IEEE Transactions on Power Delivery, 2009, 24(3):1703-1709.
[6] XU Kan, ZHANG Xiaozhi, CHEN Zhiguo, et al. Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques:a case study in Hubei Province, China[J]. International Journal of Remote Sensing, 2016, 37(20):4818-4837.
[7] DENG S, LI P, ZHANG J, et al. Power line detection from synthetic aperture radar imagery using coherence of co-polarisation and cross-polarisation estimated in the hough domain[J]. IET Radar, Sonar & Navigation, 2012, 6(9):873-880.
[8] YANG Wen, XU Ge, CHEN Jiayu, et al. Power transmission towers extraction in polarimetric SAR imagery based on genetic algorithm[M]//HUANG D S, LI K, IRWIN G W. Intelligent Computing in Signal Processing and Pattern Recognition. Berlin:Springer, 2006, 345:410-420.
[9] 赵浩程, 雷俊峰, 王先培, 等. 背景复杂下航拍图像的电力线识别算法[J]. 测绘通报, 2019(7):28-32. DOI:10.13474/j.cnki.11-2246.2019.0213. ZHAO Haocheng, LEI Junfeng, WANG Xianpei, et al. Power line identification algorithm for aerial image in complex background[J]. Bulletin of Surveying and Mapping, 2019(7):28-32. DOI:10.13474/j.cnki.11-2246.2019.0213.
[10] JÓŹKÓW G, VANDER J B, TOTH C, et al. Experiments with UAS imagery for automatic modeling of power line 3D geometry[C]//The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Toronto, Canada:[s. n.], 2015:403-409.
[11] 张文峰, 彭向阳, 陈锐民, 等. 基于无人机红外视频的输电线路发热缺陷智能诊断技术[J]. 电网技术, 2014(5):1334-1338. ZHANG Wenfeng, PENG Xiangyang, CHEN Ruimin, et al. Intelligent diagnostic techniques of abnormal heat defect in transmission lines based on unmanned helicopter infrared video[J]. Power System Technology, 2014(5):1334-1338.
[12] JADIN M S, GHAZALI K H, TAIB S. Thermal condition monitoring of electrical installations based on infrared image analysis[C]//Proceedings of 2013 Saudi International Electronics, Communications and Photonics Conference. Fira, Greece:IEEE, 2013:1-6.
[13] 林祥国, 张继贤. 架空输电线路机载激光雷达点云电力线三维重建[J]. 测绘学报, 2016, 45(3):347-353. LIN Xiangguo, ZHANG Jixian. 3D power line reconstruction from airborne LiDAR point cloud of overhead electric power transmission corridors[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(3):347-353.
[14] 刘晓磊, 郑顺义, 许磊. 基于LiDAR点云数据的电力线自动提取算法研究[J]. 测绘地理信息, 2017, 42(4):13-16. LIU Xiaolei, ZHENG Shunyi, XU Lei. Power line automatic extraction algorithm based on LiDAR data[J]. Journal of Geomatics, 2017, 42(4):13-16.
[15] 杨必胜, 梁福逊, 黄荣刚. 三维激光扫描点云数据处理研究进展、挑战与趋势[J]. 测绘学报, 2017, 46(10):1509-1516. DOI:10.11947/j.AGCS.2017.20170351. YANG Bisheng, LIANG Fuxu, HUANG Ronggang. Progress, challenges and perspectives of 3D LiDAR point cloud processing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1509-1516. DOI:10.11947/j.AGCS.2017.20170351.
[16] MATIKAINEN L, LEHTOMÄKI M, AHOKAS E, et al. Remote sensing methods for power line corridor surveys[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119:10-31.
[17] JWA Y, SOHN G. A multi-level span analysis for improving 3D power-line reconstruction performance using airborne laser scanning data[C]//Photogrammetric Computer Vision and Image Analysis. Saint-Mande, France:[S.l.], 2012:1-3.
[18] 陈驰, 麦晓明, 宋爽, 等. 机载激光点云数据中电力线自动提取方法[J]. 武汉大学学报(信息科学版), 2015, 40(12):1600-1605. CHEN Chi, MAI Xiaoming, SONG Shuang, et al. Automatic power lines extraction method from airborne LiDAR point cloud[J]. Geomatics and Information Science of Wuhan University, 2015, 40(12):1600-1605.
[19] KWOCZYHSKA B, DOBEK J. Elaboration of the 3D model and survey of the power lines using data from airborne laser scanning[J]. Journal of Ecological Engineering, 2016, 17(4):65-74.
[20] 刘正军, 梁静, 张继贤. 空间域分割的机载LiDAR数据输电线快速提取[J]. 遥感学报, 2014, 18(1):61-76. LIU Zhengjun, LIANG Jing, ZHANG Jixian. Power lines extraction from airborne LiDAR data using spatial domain segmentation[J]. Journal of Remote Sensing, 2014, 18(1):61-76.
[21] 余洁, 穆超, 冯延明, 等. 机载LiDAR点云数据中电力线的提取方法研究[J]. 武汉大学学报(信息科学版), 2011, 36(11):1275-1279. YU Jie, MU Chao, FENG Yanming. Powerlines extraction techniques from airborne LiDAR data[J]. Geomatics and Information Science of Wuhan University, 2011, 36(11):1275-1279.
[22] 张从新, 赵乐, 王先培. 复杂地物背景下电力线的快速提取算法[J]. 武汉大学学报(工学版), 2018, 51(8):732-739. ZHANG Congxin, ZHAO Le, WANG Xianpei. Research on fast extraction algorithm of power line in complex ground object background[J]. Engineering Journal of Wuhan University, 2018, 51(8):732-739.
[23] GUAN Haiyan, YU Yongtao, LI J, et al. Extraction of power-transmission lines from vehicle-borne LiDAR data[J]. International Journal of Remote Sensing, 2016, 37(1):229-247.
[24] GUO Bo, HUANG Xianfeng, ZHANG Fan, et al. Classification of airborne laser scanning data using JointBoost[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 100:71-83.
[25] 周汝琴, 许志海, 彭炽刚, 等. 一种高压输电走廊机载激光点云分类方法[J]. 测绘科学, 2019, 44(3):21-27, 33. ZHOU Ruqin, XU Zhihai, PENG Zhigang, et al. A JointBoost-based classification method of high voltage transmission corridor from airborne LiDAR point cloud[J]. Science of Surveying and Mapping, 2019, 44(3):21-27, 33.
[26] 宣伟, 花向红, 邹进贵, 等. 自适应最优邻域尺寸选择的点云法向量估计方法[J]. 测绘科学, 2019, 44(10):101-108, 116. XUAN Wei, HUA Xianghong, ZOU Jingui, et al. A new method of normal estimation for point cloud based on adaptive optimal neighborhoods[J]. Science of Surveying and Mapping, 2019, 44(10):101-108, 116.
[27] 周志华. 机器学习[M]. 北京:清华大学出版社, 2016. ZHOU Zhihua. Machine learning[M]. Beijing:Tsinghua University Press, 2016.
[28] 孟遂民, 孔伟. 架空输电线路设计[M]. 北京:中国电力出版社, 2007. MENG Suiming, KONG Wei. Design of overhead transmission line[M]. Beijing:China Electric Power Press, 2007.
[29] 麻卫峰, 王金亮, 王成, 等. 机载激光雷达点云数据输电线路弧垂模拟[J]. 测绘科学技术学报, 2019, 36(4):394-399, 405. MA Weifeng, WANG Jinliang, WANG Cheng, et al. Transmission line sag simulation from airborne LiDAR point cloud data[J]. Journal of Geomatics Science and Technology, 2019, 36(4):394-399, 405.
[30] 张继贤, 段敏燕, 林祥国, 等. 激光雷达点云电力线三维重建模型的对比与分析[J]. 武汉大学学报(信息科学版), 2017, 42(11):1565-1572. ZHANG Jixian, DUAN Minyan, LIN Xiangguo, et al. Comparison and analysis of models for 3D power line reconstruction using LiDAR point cloud[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11):1565-1572.