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Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach

Color; Electric lines; Electric power transmission; Optical radar; Satellite imagery; Space-based radar; Synthetic aperture radar; Transmissions; Vector spaces; Vegetation; Classification accuracy; Environmental challenges; Gray level co occurrence matrix(GLCM); Light detection and ranging; Power in...

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Main Authors: Mahdi Elsiddig Haroun F., Mohamed Deros S.N., Bin Baharuddin M.Z., Md Din N.
Other Authors: 57218938188
Format: Article
Published: MDPI AG 2023
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spelling my.uniten.dspace-261562023-05-29T17:07:17Z Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach Mahdi Elsiddig Haroun F. Mohamed Deros S.N. Bin Baharuddin M.Z. Md Din N. 57218938188 57188721836 35329255600 9335429400 Color; Electric lines; Electric power transmission; Optical radar; Satellite imagery; Space-based radar; Synthetic aperture radar; Transmissions; Vector spaces; Vegetation; Classification accuracy; Environmental challenges; Gray level co occurrence matrix(GLCM); Light detection and ranging; Power interruptions; Statistical moments; Support vector machine algorithm; Vegetation density; Support vector machines Vegetation encroachment along electric power transmission lines is one of the major environmental challenges that can cause power interruption. Many technologies have been used to detect vegetation encroachment, such as light detection and ranging (LiDAR), synthetic aperture radar (SAR), and airborne photogrammetry. These methods are very effective in detecting vegetation encroachment. However, they are expensive with regard to the coverage area. Alternatively, satellite imagery can cover a wide area at a relatively lower cost. In this paper, we describe the statistical moments of the color spaces and the textural features of the satellite imagery to identify the most effective features that can increase the vegetation density classification accuracy of the support vector machine (SVM) algorithm. This method aims to distinguish between high-and low-density vegetation regions along the power line corridor right-of-way (ROW). The results of the study showed that the statistical moments of the color spaces contribute positively to the classification accuracy while some of the gray level co-occurrence matrix (GLCM) features contribute negatively to the classification accuracy. Therefore, a combination of the most effective features was used to achieve a recall accuracy of 98.272%. � 2021 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T09:07:17Z 2023-05-29T09:07:17Z 2021 Article 10.3390/en14123393 2-s2.0-85108378867 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108378867&doi=10.3390%2fen14123393&partnerID=40&md5=541ba6c56c8ac923d23a576b522ad03b https://irepository.uniten.edu.my/handle/123456789/26156 14 12 3393 All Open Access, Gold MDPI AG Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Color; Electric lines; Electric power transmission; Optical radar; Satellite imagery; Space-based radar; Synthetic aperture radar; Transmissions; Vector spaces; Vegetation; Classification accuracy; Environmental challenges; Gray level co occurrence matrix(GLCM); Light detection and ranging; Power interruptions; Statistical moments; Support vector machine algorithm; Vegetation density; Support vector machines
author2 57218938188
author_facet 57218938188
Mahdi Elsiddig Haroun F.
Mohamed Deros S.N.
Bin Baharuddin M.Z.
Md Din N.
format Article
author Mahdi Elsiddig Haroun F.
Mohamed Deros S.N.
Bin Baharuddin M.Z.
Md Din N.
spellingShingle Mahdi Elsiddig Haroun F.
Mohamed Deros S.N.
Bin Baharuddin M.Z.
Md Din N.
Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach
author_sort Mahdi Elsiddig Haroun F.
title Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach
title_short Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach
title_full Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach
title_fullStr Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach
title_full_unstemmed Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach
title_sort detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: a features analysis approach
publisher MDPI AG
publishDate 2023
_version_ 1806427395411935232
score 13.211869

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