Earticle

초록

영어

In heterogeneous landscapes, high-resolution land cover classification is vital for planning, ecological monitoring, and green infrastructure management. This study evaluates the performance of five machine learning algorithms: Decision Tree (DT), Naïve Bayes, Support Vector Machine (SVM), Random Tree (RT), and K-Nearest Neighbors (KNN), using UAV multispectral imagery and object-based image analysis (OBIA). Five scenarios were designed to compare algorithm accuracy. Additionally, a sixth scenario applied the best-performing algorithm to a feature subset selected through Recursive Feature Elimination (RFE), to examine the effect of feature optimization. RT achieved the highest overall classification accuracy (76.75%) and Kappa coefficient (0.7066), while SVM showed limited performance in complex environments. Height features contributed most to accuracy improvements, followed by spectral and geometric features. In class-specific analysis, the Naïve Bayes algorithm yielded the highest Producer’s Accuracy (90.86%) for forest-type land cover but had a lower User’s Accuracy (70.41%), indicating overclassification. In contrast, RT showed more balanced performance (PA = 87.09%, UA = 85.71%), suggesting greater reliability. The results demonstrate the benefits of integrating algorithm selection with feature optimization to improve classification accuracy in complex settings. This approach provides methodological insights for fine-scale mapping of vegetated areas and supports future applications in landscape monitoring and urban green space assessment.

키워드

저자

• Zhien Li [ Department of Landscape Architecture, Graduate School, Kangwon National University ]
• Sung-Ho Kil [ Department of Ecological Landscape Architecture Design, Kangwon National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국산림공학회 학술대회

• 간기

  연간

• 수록기간

  2023~2025

• 십진분류

  KDC 526 DDC 634