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

• Published in: Energies (Basel) Vol. 14; no. 12; p. 3393
• Main Authors: Mahdi Elsiddig Haroun, Fathi, Mohamed Deros, Siti Noratiqah, Bin Baharuddin, Mohd Zafri, Md Din, Norashidah
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2021
• Subjects: Airborne radar; Classification; Color; Datasets; Electric power; Electric power transmission; Encroachment; Methods; Photogrammetry; Power lines; Satellite imagery; satellite images; Satellites; Statistics; Support vector machines; SVM; Synthetic aperture radar; Transmission lines; Vegetation; vegetation encroachment
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14123393

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%.