Abstract
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.
Highlights
In the past few years assessment and monitoring of power line corridor (PLC) is gaining importance and has become an area of active research
We have addressed the issue of extraction of individual pylons and wires which are very important for modelling of 3D objects in a power line corridor (PLC) map
The extraction of individual objects is important for a detailed PLC mapping
Summary
In the past few years assessment and monitoring of power line corridor (PLC) is gaining importance and has become an area of active research. The conventional methods for inspection of electric network rely on the participation of ground personnel and airborne camera to patrol power lines (PLs) and have limitations such as need of intensive labour and low efficiency. Remote sensors such as optical images, synthetic aperture radar (SAR) images and airborne laser Sacanner (ALS) data for PLC monitoring is part of an active research a days. Optical and SAR images are commonly used to extract PLC objects. These sensors have limitations such as weather dependency and occlusion. A detailed survey on PLC monitoring methods is given in (Matikainen et al, 2016)
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