## 3 Results
### Classification Results and Analysis  
Quantitative evaluations of classification accuracy using different methods are depicted in Figure 7. AdaBoost-RF integrates deep learning-based feature extraction with Random Forest (RF) as the foundational classifier, subsequently employing AdaBoost for classification. Meanwhile, 3 DCNN-NB, 3 DCNN-SVM, and 3 DCNN-RF employ deep learning for feature extraction, utilizing naive Bayes, support vector machine, and random forest as classifiers, respectively. Results demonstrate that the AdaBoost-RF method, utilizing feature selection via the 3 DCNN deep learning model followed by AdaBoost classification, attained overall accuracies of 0.9284 and 0.9383 in the respective experimental regions. Notably, this approach outperformed the other three classifiers, surpassing the 3 DCNN-SVM method by 3% and the 3 DCNN-NB method by more than 10%. This enhancement can be attributed to the efficacy of Random Forest in managing large input variables and evaluating their significance in category determination. Furthermore, in comparison to the 3 DCNN-RF method without ensemble learning, AdaBoost-RF exhibited increases of 0.2% and 0.5% in accuracy in the two experimental regions, respectively, emphasizing the  
Figure 6: Accuracy change trend as the number of features changes.  
efficacy of ensemble learning classifiers in augmenting the classification performance of base classifiers.  
Figure 8 depicts the final classification maps of the South and East experimental areas. Figure 9 depicts the comparative outcomes of different methods in two specific regions, illustrating the land cover and forest distribution achieved through our approach (AdaBoost-RF). The specific positions of case 1 and case 2 in Figure 9 are indicated in Figure 8. Visually, there are no significant misclassifications observed at a large regional scale. Our approach's results closely match the land cover distribution observed in Sentinel-2 imagery, demonstrating significantly fewer misclassification patches compared to the other three methods. AdaBoost-RF shows a slight improvement in classification accuracy compared to 3 DCNN-RF, as shown in Figure 9, accompanied by a decrease in localized misclassifications. Overall, our approach accurately identifies forests in land cover classification results, offering precise spatial distribution of forest resources. This study demonstrates the efficacy of 3 DCNN in extracting deep image features, improving classification performance. Ultimately, the integration of deep learning-based feature extractor and ensemble learning classifiers produces the most optimal visual classification outcomes.  
Figure 7: Comparative experiment on forest classification. (**a**) Results of South; (**b**) results of East.  
Figure 8: Overall classification maps in the experimental areas.  
Figure 9: Classification maps in the case areas. (**a**) Case area 1; (**b**) Case area 2.  
This study utilized the proposed approach for nationwide classification. Following repeated experiments in two specific regions, we acquired optimal feature selection results, network models, and parameters. Subsequently, we applied the same methodology to gather nationwide samples and validation data. By employing this approach, we created a national land cover map. Forest resources are crucial natural assets in Czech; thus, our analysis concentrated on forest resources utilizing land classification. Validation points were chosen from the intersection set that had been processed earlier. Unreasonable sample points were eliminated, resulting in the selection of 2563 validation points. As depicted in Figure 10, the results obtained nationwide yielded a total accuracy of 90.98%, the user accuracy of forest is 91.97%, and the producer accuracy is 96.20%. Additionally, user and producer accuracy for other land types were as follows: cropland, 90.51% and 96.60%; grass, 50.00% and 5.48%; shrub, 100.00% and 3.33%; wetland, 0% and 0%; water, 92.31% and 85.71%; impervious, 90.24% and 62.18%.