میز خدمت الکترونیک
دوره ویراستاری

  اخبار و اعلانات

 آمار

تعداد نشریات126
تعداد شماره‌ها7,094
تعداد مقالات76,235
تعداد مشاهده مقاله151,659,225
تعداد دریافت فایل اصل مقاله113,765,985
Shokri, Danesh, Rastiveis, Heidar, Sheikholeslami, Seyed Mohammad, Shahhoseini, Reza, Li, Jonathan. (1400). Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area. , 5(2), 63-73. doi: 10.22059/eoge.2022.317348.1092
Danesh Shokri; Heidar Rastiveis; Seyed Mohammad Sheikholeslami; Reza Shahhoseini; Jonathan Li. "Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area". , 5, 2, 1400, 63-73. doi: 10.22059/eoge.2022.317348.1092
Shokri, Danesh, Rastiveis, Heidar, Sheikholeslami, Seyed Mohammad, Shahhoseini, Reza, Li, Jonathan. (1400). 'Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area', , 5(2), pp. 63-73. doi: 10.22059/eoge.2022.317348.1092
Shokri, Danesh, Rastiveis, Heidar, Sheikholeslami, Seyed Mohammad, Shahhoseini, Reza, Li, Jonathan. Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area. , 1400; 5(2): 63-73. doi: 10.22059/eoge.2022.317348.1092

Fast extraction of power lines from mobile LiDAR point clouds based on SVM classification in non-urban area

Earth Observation and Geomatics Engineering
مقاله 1، دوره 5، شماره 2، اسفند 2021، صفحه 63-73    اصل مقاله (883.71 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22059/eoge.2022.317348.1092
نویسندگان
Danesh Shokri1؛ Heidar Rastiveis* 1؛ Seyed Mohammad Sheikholeslami2؛ Reza Shahhoseini1؛ Jonathan Li3
1Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Eng., University of Tehran, Tehran, Iran
2Department of Communications, School of Electrical and Computer Eng., Faculty of Eng., University of Tehran, Tehran, Iran
3Dept. of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada
چکیده
Mobile Laser Scanning (MLS) systems have been used for power line inspection in a fast and precise fashion. However, manually processing of huge LiDAR point clouds is tedious and time-consuming. Thus, an automated method is needed. This study proposes a machine learning-based method for automated detection of power lines from MLS point clouds.  The proposed method consists of three main steps: pre-processing, line extraction using Support Vector Machine (SVM), and post-extraction. In the pre-processing step, noisy and low-height points are eliminated after sectioning the collected point clouds. This step considerably reduces the volume of point clouds by 90%. Then, the point features including linearity, planarity, verticality, and the largest component of Principal Component Analysis (PCA) are used as the best-fitted descriptors for power line detection. After training the SVM by a small section of points, SVM properly classified the point clouds with about 97% and 98% accuracies regarding precision and recall, respectively. In the final step, a post-extraction is required to eliminate false points in the power line class. This step improved the recall from 98% to 99.4% and decreased slightly the precision accuracy from 97% to 95.5%. The results demonstrated that the proposed method works rapidly, about 14 seconds per section with an average of 5 million points in each section.
کلیدواژه‌ها
Point Clouds؛ Support Vector Machines (SVM)؛ Power Line Extraction؛ Cables؛ Mobile Laser Scanner (MLS)
آمار
تعداد مشاهده مقاله: 858
تعداد دریافت فایل اصل مقاله: 876





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب