A Hierarchical unsupervised method for power line classification from airborne LiDAR data

作     者：Yanjun Wang Qi Chen Lin Liu Kai Li 

作者机构：National-local Joint Engineering Laboratory of Geo-spatial Information TechnologyHunan University of Science and TechnologyXiangtanPeople’s Republic of China School of Resource Environment and Safety EngineeringHunan University of Science and TechnologyXiangtanPeople’s Republic of China Department of Geography and EnvironmentUniversity of Hawai’i at MānoaHonoluluHIUSA Department of GeographyUniversity of CincinnatiCincinnatiOHUSA School of Geographic ScienceCenter of Geo-Informatics for Public SecurityGuangzhou UniversityGuangzhouPeople’s Republic of China 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2019年第12卷第12期

页      面：1406-1422页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：the National Natural Science Foundation of China(grant numbers 41601426 and 41771462) the Hunan Provincial Natural Science Foundation(grant number 2018JJ3155) the Open Foundation of Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Map-ping and Geoinformation,Wuhan University(grant number GCWD201806) the China Scholarship Council(grant number 201708430040) 

主　　题：Airborne LiDAR data power lines unsupervised classification feature extraction 

摘      要：The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply *** methods for power line classification can be either supervised or *** methods might achieve high accuracy for small areas,but it is time consuming to collect training data over areas of different conditions and ***,unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great *** this paper,we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points(including the power line corridor direction detection based on a layered Hough transform,connectivity analysis,and Douglas–Peucker simplification algorithm),followed by the extraction of contextual linear and angular features for each candidate laser points,and finally by setting the feature threshold values to identify the power line *** tested the method over both forest and urban areas and found that the precision,recall and quality rates were up to 96.7%,88.8%and 78.3%,respectively,for the test datasets and were higher than the ones from a previously developed supervised classification ***,our approach has the advantages of achieving relatively high accuracy and being relatively fast.