Volume IV-4/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 81-87, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-81-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 81-87, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-81-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Nov 2017

13 Nov 2017

CLASSIFIER-FREE DETECTION OF POWER LINE PYLONS FROM POINT CLOUD DATA

M. Awrangjeb and M. K. Islam M. Awrangjeb and M. K. Islam
  • Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia

Keywords: Power line extraction, transmission line extraction, pylon detection, power line reconstruction, LIDAR, point cloud data

Abstract. High density airborne point cloud data has become an important means for modelling and maintenance of a power line corridor. Since, the amount of data in a dense point cloud is huge even in a small area, an automatic detection of pylons in the corridor can be a prerequisite for efficient and effective extraction of wires in a subsequent step. However, the existing solutions mostly overlook this important requirement by processing the whole data into one go, which nonetheless will hinder their applications to large areas. This paper presents a new pylon detection technique from point cloud data. First, the input point cloud is divided into ground and nonground points. The non-ground points within a specific low height region are used to generate a pylon mask, where pylons are found stand-alone, not connected with any wires. The candidate pylons are obtained using a connected component analysis in the mask, followed by a removal of trees by comparing area, shape and symmetry properties of trees and pylons. Finally, the parallelism property of wires with the line connecting pair of candidate pylons is exploited to remove trees that have the same area and shape properties as pylons. Experimental results show that the proposed technique provides a high pylon detection rate in terms of completeness (100 %) and correctness (100 %).