Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

Short summary

Support Vector Machine (SVM) achieved the highest land cover classification accuracy (90-95% OA) using Sentinel-2 imagery, showing the least sensitivity to training sample size compared to Random Forest (RF) and k-Nearest Neighbor (kNN).

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