Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection

Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection

The land use land cover (LULC) map is extensively employed for different purposes. Machine learning (ML) algorithms applied in remote sensing (RS) data have been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that random forest (RF)...

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Bibliographic Details
Main Authors: Yan-Cheng Tan, Lia Duarte, Ana Cláudia Teodoro
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Land
Subjects:
machine learning
land cover classification
object-based image analysis
pixel-based image analysis
random forest
support vector machine
Online Access:https://www.mdpi.com/2073-445X/13/11/1878
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