Abstract
Abstract. Monitoring vegetation encroachment under overhead high voltage power line is a challenging problem for electricity distribution companies. Absence of proper monitoring could result in damage to the power lines and consequently cause blackout. This will affect electric power supply to industries, businesses, and daily life. Therefore, to avoid the blackouts, it is mandatory to monitor the vegetation/trees near power transmission lines. Unfortunately, the existing approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation/trees near or under the power transmission poles using satellite stereo images, which were acquired using Pleiades satellites. The 3D depth of vegetation has been measured near power transmission lines using stereo algorithms. The area of interest scanned by Pleiades satellite sensors is 100 square kilometer. Our dataset covers power transmission poles in a state called Sabah in East Malaysia, encompassing a total of 52 poles in the area of 100 km. We have compared the results of Pleiades satellite stereo images using dynamic programming and Graph-Cut algorithms, consequently comparing satellites’ imaging sensors and Depth-estimation Algorithms. Our results show that Graph-Cut Algorithm performs better than dynamic programming (DP) in terms of accuracy and speed.
Highlights
Vegetation or trees may pose a major risk to the reliability of transmission power lines (Jones, 2001)
Graph-Cut algorithm is successfully applied to Pleiades satellite stereo images
We can conclude that the ordering constraints cooperate in Graph Cut Algorithm helps in occluded area and minimize the noise as shown in disparity map
Summary
Vegetation or trees may pose a major risk to the reliability of transmission power lines (Jones , 2001). Many methods can be deployed to monitor the vegetation growth, and more importantly to estimate the height of the vegetation within the danger zone Traditional methods such as manual line patrol or inspection by foot lack accuracy primarily due to human judgmental errors [Lotti, 1994]. Videography, or aerial multispectral imaging utilizing computer vision techniques, is better than the previous two methods This method uses a helicopter or a balloon or an airborne vehicle to capture the aerial images of vegetation. This method has a better accuracy as compared with visual or video surveillance.
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