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

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 selection method with the same number of samples. Therefore, the grouping selection method performed the best. When using the grouping selection method, the image classification accuracy increased with the increase in the number of samples within a certain sample size range.

Automated summary

This paper studied the importance of selecting training samples in remote sensing image classification and compared the effectiveness of grouping selection, entropy-based selection, and direct selection. The experimental results showed that the grouping selection method achieved higher classification accuracy using fewer samples and outperformed the direct selection method. Within a certain range, increasing the number of samples can improve the accuracy of image classification.