## 5 Discussion
### Sample Equalization Method  
To solve the classification accuracy problem caused by the sample size difference between categories, we choose SMOTE method which could solve the gap problem of the sample size. However, this method cannot be directly applied into our study because the sample size difference between the categories with the largest sample number and the minimum sample size is too large, which causes most samples of the category to be interpolated and causes samples of small sample categories to lose their original basic characteristics [85].  
Although the sample set obtained from the SMOTE algorithm can yield a better training accuracy and test accuracy, it is found that the China 2010 classified products obtained by this method are quite different from the 2010 CNLUCC classified products in China. The main differences are distributed in the areas enclosed by the three rectangular boxes shown in Figure 11.  
1. The region 1 rectangular frame indicates the northeastern region of China. Among the CNLUCC land cover classification products, the main land cover types in the area are grassland and bare land, and a small amount of tundra covers the middle. This result is consistent with the actual land cover type in the area. However, in the results obtained from the SMOTE algorithm, it can be found that a large number of tundra distributions occur in western China.
2. The region 2 rectangular frame indicates the southwestern region of China. Among the CNLUCC land cover classification products, the main land cover types in the area are woodland and shrubs, in which shrubs and woodlands are intersecting. This result is consistent with the actual land cover type in the area. However, in the results obtained from the SMOTE algorithm, it can be found that a large number of dense shrubs are distributed in southwestern China. The results of the above three regions obtained through the SMOTE algorithm are obviously not consistent with the actual land cover.  
To correct this problem, this study improves the SMOTE algorithm and uses the stratified SMOTE algorithm. After inputting the dataset obtained from the algorithm into the model and after several iterations, it finds that the training accuracy and test accuracy of the land cover classification model can also achieve good results. To prove that the stratified SMOTE algorithm has a positive effect on the actual classification results, the dataset obtained from the algorithm to acquire the 2010 land cover type products was this study used. The results were compared with the 2010 products obtained from CNLUCC products and the 2010 products obtained from the SMOTE algorithm, as shown in Figure 11. The misclassification of areas within the corresponding three rectangular boxes is very well solved. In addition, the results of the two SMOTE algorithms are referenced to CNLUCC data, and the number of correctly classified cells in the 10 categories of the two algorithms is counted. The results after statistics are shown in Table 6. For each category, the stratified SMOTE algorithm improves the accuracy of the classification, and the method is simple and effective. The accuracies of the six small sample categories of shrubs, water bodies, ice and snow, wetland cities and tundra are much higher.  
Figure 11: **(a)** The CNLUCC original classification data, (**b**) the use of the stratified SMOTE algorithm, (**c**) the use of the SMOTE algorithm and (**d**) No use of the SMOTE algorithm. (**e**) Column contrast comparison, with an accuracy improvement of more than 20% and (**f**) large sample (Sample size is greater than 90 k) and small sample (Sample size is less than 90 k) category accuracy improvement trend chart.