{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T16:40:46Z","timestamp":1775320846934,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,17]],"date-time":"2018-12-17T00:00:00Z","timestamp":1545004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key Technology program of China South Power Grid","award":["GDKJQQ20161187"],"award-info":[{"award-number":["GDKJQQ20161187"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The security of high-voltage power transmission corridors is significantly vital to the national economy and daily life. With its rapid development, LiDAR (Light Detection and Ranging) technology has been widely applied in the inspection of transmission lines. As the basis of potential hazard detection, a robust and precise power line model is a necessary requirement for rapid and correct clearance. Thus, this paper proposes a novel method for high-voltage bundle conductor reconstruction, which can precisely reconstruct each sub-conductor. First, points in high-voltage power transmission corridors are detected and classified into four categories; second, for classified power lines, single power line spans are extracted, and bundle conductors are identified by analyzing the single spans\u2019 fitting residuals; and then, each sub-conductor of bundle conductors is extracted by a projected dichotomy method on the XOY and XOZ planes, respectively; finally, a double-RANSAC (random sample consensus)-based algorithm was introduced to reconstruct each power line. The proposed method makes use of the distribution of bundle conductors in high-voltage transmission lines, and our experiments showed that it could preferably reconstruct the real structure of bundle conductors robustly with a high precision better than 0.2 m.<\/jats:p>","DOI":"10.3390\/rs10122051","type":"journal-article","created":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T02:15:59Z","timestamp":1545099359000},"page":"2051","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Novel Method for High-Voltage Bundle Conductor Reconstruction from Airborne LiDAR Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7952-0521","authenticated-orcid":false,"given":"Ruqin","family":"Zhou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3162-0566","authenticated-orcid":false,"given":"Wanshou","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"San","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","article-title":"Remote sensing methods for power line corridor surveys","volume":"119","author":"Matikainen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhou, R., Jiang, W., Huang, W., Xu, B., and Jiang, S. (2017). A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9111172"},{"key":"ref_3","unstructured":"(2017, July 20). Global Transmission and Distribution Report\u2014Infrastructure, Upcoming Projects, Investments, Key Operators and Analysis to 2020. Available online: https:\/\/www.globaldata.com\/store\/search\/power\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Qin, X., Wu, G., Ye, X., Huang, L., and Lei, J. (2017). A Novel Method to Reconstruct Overhead High-Voltage Power Lines Using Cable Inspection Robot LiDAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9070753"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0378-7796(99)00037-1","article-title":"An overview of the condition monitoring of overhead lines","volume":"53","author":"Aggarwal","year":"2000","journal-title":"Electr. Power Syst. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.isprsjprs.2014.08.001","article-title":"Fusion of imaging spectroscopy and airborne laser scanning data for characterization of forest ecosystems\u2014A review","volume":"97","author":"Torabzadeh","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2018.2869542","article-title":"Voxel-Based Extraction of Transmission Lines from Airborne LiDAR Point Cloud Data","volume":"11","author":"Yang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guo, B., Li, Q., Huang, X., and Wang, C. (2016). An improved method for power-line reconstruction from point cloud data. Remote Sens., 8.","DOI":"10.3390\/rs8010036"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qin, X., Wu, G., Lei, J., Fan, F., and Ye, X. (2018). Detecting Inspection Objects of Power Line from Cable Inspection Robot LiDAR Data. Sensors (Basel), 18.","DOI":"10.3390\/s18041284"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3379","DOI":"10.1109\/TGRS.2010.2046905","article-title":"Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors","volume":"48","author":"Mills","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1038\/srep15298","article-title":"Impaired climbing and flight behaviour in Drosophila melanogaster following carbon dioxide anaesthesia","volume":"5","author":"Bartholomew","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1109\/8.901268","article-title":"Extraction of Power Line Maps from Millimeter-Wave Polarimetric SAR Images","volume":"48","author":"Sarabandi","year":"2000","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_13","first-page":"124","article-title":"Advances in applications and methodology for aerial infrared thermography","volume":"6205","author":"Stockton","year":"2004","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s00138-009-0206-y","article-title":"Toward automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved hough transform","volume":"21","author":"Li","year":"2010","journal-title":"Mach. Vis. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jiang, S., Jiang, W., Huang, W., and Yang, L. (2017). UAV-based oblique photogrammetry for outdoor data acquisition and offsite visual inspection of transmission line. Remote Sens., 9.","DOI":"10.3390\/rs9030278"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"990105","DOI":"10.1117\/12.2234848","article-title":"Extraction of power lines from mobile laser scanning data","volume":"9901","author":"Xiang","year":"2016","journal-title":"Proc. SPIE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3302","DOI":"10.3390\/rs6043302","article-title":"Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data","volume":"6","author":"Cheng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11267","DOI":"10.3390\/rs61111267","article-title":"Fully-automated power line extraction from airborne laser scanning point clouds in forest areas","volume":"6","author":"Zhu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/LGRS.2005.863390","article-title":"Extracting transmission lines from airborne LIDAR data","volume":"3","author":"McLaughlin","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"821","DOI":"10.14358\/PERS.79.9.821","article-title":"Point-based Classification of Power Line Corridor Scene Using Random Forests","volume":"79","author":"Kim","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.isprsjprs.2014.04.015","article-title":"Classification of airborne laser scanning data using JointBoost","volume":"100","author":"Guo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Q., Liu, L., Zheng, D., Li, C., and Li, K. (2017). Supervised classification of power lines from airborne LiDAR data in Urban Areas. Remote Sens., 9.","DOI":"10.3390\/rs9080771"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, T., and Yu, W. (2009, January 7\u201310). Performance evaluation of RANSAC family. Proceedings of the British Machine Vision Conference, London, UK.","DOI":"10.5244\/C.23.81"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/TPAMI.2007.70787","article-title":"Optimal randomized RANSAC. IEEE Trans","volume":"30","author":"Chum","year":"2008","journal-title":"Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1214\/aos\/1176345451","article-title":"Gauss and the Invention of Least Squares","volume":"9","author":"Stigler","year":"1981","journal-title":"Ann. Stat."},{"key":"ref_26","unstructured":"Melzer, T., and Briese, C. (2004, January 17\u201318). Extraction and Modeling of Power Lines from ALS Point Clouds. Proceedings of the 28th Work Austrian Association Pattern Recognition, Hagenberg, Austria."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liang, J., and Zhang, J. (2011). A New Power-line Extraction Method Based on Airborne LiDAR Point Cloud Data. Int. Symp. Image Data Fusion, 2\u20135.","DOI":"10.1109\/ISIDF.2011.6024293"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, Z., Lan, Z., Long, H., and Hu, Q. (2012, January 20\u201323). 3D modeling of pylon from airborne LiDAR data. Proceedings of the SPIE 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 915807, Wuhan, China.","DOI":"10.1117\/12.2063873"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11501","DOI":"10.3390\/rs70911501","article-title":"A model-driven approach for 3d modeling of pylon from airborne LiDAR data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guo, B., Huang, X., and Li, Q. (2016). A Stochastic Geometry Method for Pylon Reconstruction from Airborne LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8030243"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"169","DOI":"10.5194\/isprs-annals-III-3-169-2016","article-title":"Classification of Airborne Laser Scanning Data Using Geometric Multi-Scale Features and Different Neighbourhood Types","volume":"III-3","author":"Blomley","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, C., Yang, B., Song, S., Peng, X., and Huang, R. (2018). Automatic clearance anomaly detection for transmission line corridors utilizing UAV-Borne LIDAR data. Remote Sens., 10.","DOI":"10.3390\/rs10040613"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/01431161.2015.1125549","article-title":"Extraction of power-transmission lines from vehicle-borne lidar data","volume":"37","author":"Guan","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.14358\/PERS.78.11.1227","article-title":"A piecewise catenary curve model growing for 3D power line reconstruction","volume":"78","author":"Jwa","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_35","unstructured":"Chen, C., and Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data, University of California, Berkeley. Technical Report 666."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.isprsjprs.2015.01.016","article-title":"Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers","volume":"105","author":"Weinmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, Z., Jiang, W., Xu, B., Zhu, Q., Jiang, S., and Huang, W. (2017). A convolutional neural network-based 3D semantic labeling method for ALS point clouds. Remote Sens., 9.","DOI":"10.3390\/rs9090936"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2017.02.014","article-title":"Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data","volume":"126","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/2051\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:36:51Z","timestamp":1775317011000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/2051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,17]]},"references-count":38,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["rs10122051"],"URL":"https:\/\/doi.org\/10.3390\/rs10122051","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,17]]}}}