Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 165-169, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-165-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 165-169, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-165-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

AIRBORNE LIDAR POWER LINE CLASSIFICATION BASED ON SPATIAL TOPOLOGICAL STRUCTURE CHARACTERISTICS

Y. Wang1, Q. Chen2, K. Li1, D. Zheng1, and J. Fang1 Y. Wang et al.
  • 1National-local Joint Engineering Laboratory of Geo-spatial Information Technology, Hunan University of Science and Technology, 411201 Xiangtan, Hunan, China
  • 2Department of Geography, University of Hawai‘i at Mānoa, 2424 Maile Way, Honolulu, HI 96822 USA

Keywords: Airborne lidar, urban power line, neighbourhood selection, spatial topological feature, structure characteristics, power line classification

Abstract. Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.