Comparing Three Methods of Selecting Training Samples in Supervised Classification of Multispectral Remote Sensing Images.

ABSTRACT: Selecting training samples is crucial in remote sensing image classification. In this paper, we selected three images-Sentinel-2, GF-1, and Landsat 8-and employed three methods for selecting training samples: grouping selection, entropy-based selection, and direct selection. We then used the selected training samples to train three supervised classification models-random forest (RF), support-vector machine (SVM), and k-nearest neighbor (KNN)-and evaluated the classification results of the three images. According to the experimental results, the three classification models performed similarly. Compared with the entropy-based method, the grouping selection method achieved higher classification accuracy using fewer samples. In addition, the grouping selection method outperformed the direct se

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10610573 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature