Power LinesVegetation enchroachment monitoring based on Satellite Stereo images using stereo matching

Qayyum, Abdul; Malik, Aamir Saeed; Saad, Mohamad Naufal Mohamad; Iqbal, M.; Abdullah, M. F.; Abdullah, Tuan Ab Rashid Bin Tuan; Ramli, Ahmad Qisti

doi:10.1109/icsima.2014.7047425

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Disparity maps are produced in matching stereo pairs. Several different techniques that have been used to generate disparity maps such as Sum of Absolute Difference (SAD), Sum of Squared Difference (SSD), Zero Normalized Cross Correlation (ZNCC) and Normalized Cross Correlation (NCC) [17]. SAD was considered as the simplest cost function.…”

Section: Stereo Matching Detectionmentioning

confidence: 99%

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Haroun¹

2020

IJATCSE

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Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on economic of power utilities companies as well as the consumer. Vegetation detection and monitoring along the power line corridor right-of-way (ROW) are implemented to protect power transmission lines from vegetation penetration. There were various methods used to monitor the vegetation penetration, however, most of them were too expensive and time consuming. Satellite images can play a major role in vegetation monitoring, because it can cover high spatial area with relatively low cost. In this paper, the current techniques used to detect the vegetation encroachment using satellite images are reviewed and categorized into four sectors; Vegetation Index based method, object-based detection method, stereo matching based and other current techniques. However, the current methods depend usually on setting manually serval threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence, in addition to review the current technique of vegetation penetration monitoring in power transmission, the potential of using Machine Learning based algorithms are also included.

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Section: Stereo Matching Detectionmentioning

confidence: 99%

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Haroun¹

2020

IJATCSE

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“…Conventional monitoring and warnings of wildfire are mainly based on patrols of the corridor performed by professional staff, which is intensive and time consuming. Due to the wide-area observation and rapid progress in recent years, remote sensing (RS) and geographic information system (GIS) technologies could be used as effective and accessible methods for monitoring and prevention of wildfire accidents in power line corridors (Qayyum et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In order to make reasonable management for vegetation encroachment, a species map of trees in corridor has been conducted through different classification methods based on high spatial resolution hyperspectral imagery and light detection and ranging (lidar) data (Frank et al 2010). Another method for identifying trees and plants along the overhead transmission lines right of way is based on the 3D matching of multispectral stereo pairs of satellite images, which can achieve a comparable result with lidar data (Qayyum et al 2014;Kobayashi et al 2009). Then, high-resolution hyperspectral satellite images and lidar data can be combined for vegetation management (Mills et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province, China

Zhang

Chen

et al. 2016

International Journal of Remote Sensing

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Wildfires are a significant problem for power line maintenance that can lead to widespread power outages and economic loss in China. Remote sensing (RS) and geographic information system (GIS) technologies and their derived applications are effective tools for power line surveillance and disaster prevention. Using multi-source data and remote-sensing techniques, a risk assessment of wildfire occurrence in high-voltage power line corridors in Hubei Province, China, is presented in this study. Both natural and human causes are considered in the assessment, including a traditional Chinese spring custom. Historical ignition points along power line corridors in Hubei Province that occurred from 2009 to 2014 were collected as training data to create a quantitative analysis. Next, a logistic regression model was applied, and ignition probability maps were produced for power lines. Several wildfire accidents that occurred in 2015 were used as validation data to evaluate the performance of the proposed method. The experimental results indicated that the drought conditions significantly influenced wildfires as natural cause, and human activities play important roles in causing wildfires during specific time periods. The effectiveness and robustness of our method is thereby demonstrated, and our method can be used to provide valuable suggestions for wildfire management for the electrical department in Hubei Province.

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Reduction of wildfire hazard by automated monitoring of vegetation interference with power lines: point cloud analysis combined with cable mechanics

Takhirov

Исраилов

2020

J Civil Struct Health Monit

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Power LinesVegetation enchroachment monitoring based on Satellite Stereo images using stereo matching

Cited by 7 publications

References 7 publications

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province, China

Reduction of wildfire hazard by automated monitoring of vegetation interference with power lines: point cloud analysis combined with cable mechanics

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