Detection of Vegetation Encroachment in Power Transmission Line Corridor from Satellite Imagery Using Support Vector Machine: A Features Analysis Approach

Fathi Mahdi Elsiddig Haroun, Siti Noratiqah Mohamed Deros, Mohd Zafri Bin Baharuddin and Norashidah Md Din
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Fathi Mahdi Elsiddig Haroun: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia
Siti Noratiqah Mohamed Deros: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia
Mohd Zafri Bin Baharuddin: College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Malaysia
Norashidah Md Din: Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang 43000, Malaysia

Energies, 2021, vol. 14, issue 12, 1-16

Abstract: 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%.

Keywords: satellite images; SVM; vegetation encroachment; transmission lines (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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