- Title
- Voxel-based extraction of individual pylons and wires from lidar point cloud data
- Creator
- Munir, Nosheen; Awrangjeb, Mohammad; Stantic, Bela; Lu, Guojun; Islam, Syed
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180873
- Identifier
- vital:15827
- Identifier
-
https://doi.org/10.5194/isprs-annals-IV-4-W8-91-2019
- Identifier
- ISBN:2194-9050
- Abstract
- Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness.
- Publisher
- Gottingen: Copernicus GmbH
- Relation
- ISPRS annals of the photogrammetry, remote sensing and spatial information sciences Vol. IV-4/W8, no. (2019), p. 91-98
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.
- Rights
- Open Access
- Subject
- Evaluation; Feature extraction; Lidar; Object recognition; Power lines; Pylons; Support vector machines; Three dimensional models; Vegetation
- Full Text
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