## 4 Object-Oriented Classification Process
### Comparison of Classification Results  
As shown in Figure 5 below, based on the four classification methods, the trend of overall accuracy was obtained by continuously increasing the number of feature fields, and the feature selection methods were compared. As the number of features increased, the overall accuracy gradually improved. When the number of features reached about 15, the classification accuracy decreased slightly with an increase in the number of features, then remained stable. Therefore, in the process of object-oriented classification experiments, when too many features are involved in the classification, it may not be possible to achieve the optimal classification results, and instead the classification accuracy and classification efficiency are reduced. Overall, filtered feature selection methods presented better results than wrapped methods, while embedded feature selection methods presented the worst results. Furthermore, the SVM classification results were relatively stable, and the impact of different feature selection methods on the classification accuracy was smaller than the use of other classification methods.  
Figure 5: Cont.  
According to Figure 6, the proposed Fm was found to have higher overall accuracy than the other feature selection methods with both RF and kNN classifiers, with accuracies of 95.18% and 96.14%, respectively. Although it was not optimal with the CART and SVM classifiers, the overall accuracy still achieved good results. This indicates that the combined scheme of Fisher Score and mRMR algorithm can obtain high-accuracy classification results with specific classifiers and can outperform both wrapped and filter feature selection methods.  
As shown in the bar chart, the overall accuracy of Fm is better than the other four feature selection methods. In the CART classifier, the overall accuracy of Fm is 3.28%, 5.27%, and 6.61% higher than mRMR, RFE, and LR, respectively, and 0.41% lower than Fisher Score. In the SVM classifier experiment, the overall accuracy of Fm is higher than Fisher Score, RFE, and LR, respectively, and the overall accuracy of Fm is 0.39% lower than mRMR. Among RT and KNN classifiers, Fm achieves the highest overall accuracy, which is 0.38%, 3.03%, 2.12%, and 6.94% higher than Fisher Score, mRMR, REF, and LR and 0.58%, 3.18%, 2.12%, and 5.78% higher than Fisher Score, mRMR, and LR, respectively.  
In the case of limited samples, excessive features do not improve the classification accuracy of the image. However, the classification accuracy can be improved to a certain extent by taking  
Figure 5: The variation trend of number of features and overall accuracy of five feature selection methods with different classifiers: (**a**) CART; (**b**) RF; (**c**) KNN; (**d**) SVM.  
Figure 6: The optimal accuracy of four features with different classifiers was selected.  
into account the correlation between features through the mRMR algorithm or only considering the separability of a single feature through the Fisher Score algorithm. Our experiments showed that the proposed Fm method can effectively improve the classification accuracy of high-resolution remote sensing images with the RF and \(k\)NN classifiers.