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

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dc.citedby1
dc.contributor.authorMahdi Elsiddig Haroun F.en_US
dc.contributor.authorMohamed Deros S.N.en_US
dc.contributor.authorBin Baharuddin M.Z.en_US
dc.contributor.authorMd Din N.en_US
dc.contributor.authorid57218938188en_US
dc.contributor.authorid57188721836en_US
dc.contributor.authorid35329255600en_US
dc.contributor.authorid9335429400en_US
dc.date.accessioned2023-05-29T09:07:17Z
dc.date.available2023-05-29T09:07:17Z
dc.date.issued2021
dc.descriptionColor; 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 machinesen_US
dc.description.abstractVegetation 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3393
dc.identifier.doi10.3390/en14123393
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85108378867
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85108378867&doi=10.3390%2fen14123393&partnerID=40&md5=541ba6c56c8ac923d23a576b522ad03b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26156
dc.identifier.volume14
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleDetection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approachen_US
dc.typeArticleen_US
dspace.entity.typePublication
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