Evaluating the accuracy of manual classification in satellite images using supervised algorithms

Abstract

This research focused on evaluating the manual classification of land cover using Sentinel-2 imagery. Supervised algorithms were applied to validate and improve this process. Three algorithms were selected based on their computational efficiency: K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). The results show that KNN achieved optimal performance, demonstrating a solid balance between accuracy, F1 score, and execution time compared to RF. RF, for its part, obtained greater accuracy, indicating its superior ability to correctly identify classes; however, it requires more computational resources. SVM exhibited lower performance in the evaluated metrics but achieved a shorter execution time. It was identified as the algorithm with the greatest limitations for separating classes within this dataset derived from the different study areas. Overall, the comparison confirmed that the manual classifications developed in QGIS are supported and validated by the application of these supervised methods. The use of such algorithms contributes to improving the accuracy, consistency, and efficiency of geospatial classification tasks.