Remote sensing methods for power line corridor surveys

Matikainen, Leena; Lehtomäki, Matti; Ahokas, Eero; Hyyppä, Juha; Karjalainen, Mika; Jaakkola, Anttoni; Kukko, Antero; Heinonen, Tero

doi:10.1016/j.isprsjprs.2016.04.011

Cited by 312 publications

(211 citation statements)

References 78 publications

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“…Power lines have unique geographical characteristics as follows: distributed regularly between two neighboring electric towers or poles with sag and other physical properties [2,6,13,41]. This knowledge can be used to screen power lines in large LiDAR data scene.…”

Section: Power Line Corridor Direction Constructionmentioning

confidence: 99%

“…Inspection of power lines to detect and eliminate hidden risks is an important task for urban and rural power supply management and scientific planning [1,2]. However, power lines stretch across long geographical distances, making it labor-intensive and costly to conduct traditional field-based inspection.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7]; (ii) Hough transform and clustering based on 2D image processing [5,[8][9][10][11]; (iii) supervised classification based on geometrical and distribution features of laser points [2,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

et al. 2017

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Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy.

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Section: Power Line Corridor Direction Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

et al. 2017

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“…There are seven fundamental types of data that can be used for the inspection of power line corridors [3]: synthetic aperture radar (SAR) images, optical satellite images, optical aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) images. SAR image pixels include the radar backscattering intensity, the phase of the backscattering signal and the range from the sensor to the target.…”

Section: Introductionmentioning

confidence: 99%

Automatic Power Line Inspection Using UAV Images

et al. 2017

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Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m.

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“…Thus, the LIDAR technology has made the entire survey more effective and efficient. A recent comprehensive review of various types of PLC surveying methods can be found in Matikainen et al (Matikainen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Classifier-Free Detection of Power Line Pylons From Point Cloud Data

Awrangjeb

Islam

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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%).

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Remote sensing methods for power line corridor surveys

Cited by 312 publications

References 78 publications

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

Automatic Power Line Inspection Using UAV Images

Classifier-Free Detection of Power Line Pylons From Point Cloud Data

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