## 4 Object-Oriented Classification Process
### Classification Results  
For further analysis, the optimal combinations of the four classification methods and five feature selection methods were selected--Fisher-CART, Fm-RF, Fm-\(k\)NN, and mRMR-SVM--and the classification graphs of these four combinations are shown in Figure 7. Meanwhile, in order to analyze the accuracy of the classified ground objects, the overall accuracy, as well as kappa coefficients, were calculated to evaluate the classification results, based on the decoded flags and visually decoded sample points, as shown in Table 4, as well as the specific land-cover classification accuracies.  
Figure 7: Cont.  
Figure 7: Graph of optimal classification results for different classifiers (**a**) Fisher-CART; (**b**) Fm-RF; (**c**) Fm-KNN; (**d**) mRMR-SVM.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Fisher-CART**} & \multicolumn{2}{c}{**Fm-RF**} & \multicolumn{2}{c}{**Fm-KNN**} & \multicolumn{2}{c}{**mRMR-SVM**} \\ \hline  & Producer’s & User’s & Producer’s & User’s & Producer’s & User’s & Producer’s & User’s \\  & accuracy & accuracy & accuracy & accuracy & accuracy & accuracy & accuracy & accuracy \\ Roads & 95.00\% & 76.61\% & 97.00\% & 93.26\% & 93.00\% & 95.87\% & 97.00\% & 89.81\% \\ Buildings & 74.00\% & 91.02\% & 85.00\% & 91.40\% & 92.00\% & 91.08\% & 85.00\% & 94.44\% \\ Water & 92.93\% & 97.87\% & 93.94\% & 97.90\% & 96.96\% & 98.96\% & 97.98\% & 98.97\% \\ Bare land & 97.14\% & 94.44\% & 100\% & 90.00\% & 98.57\% & 94.52\% & 98.57\% & 97.18\% \\ Vegetation & 99.33\% & 98.68\% & 99.33\% & 99.33\% & 99.33\% & 98.67\% & 99.33\% & 98.03\% \\ Overall & 91.52\% & & 95.18\% & & 96.14\% & & 95.76\% \\ accuracy & & & & & & & & \\ Kappa & 0.8923 & & 0.939 & & 0.951 & & 0.9461 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of ground cover classification.  
From the analysis in Table 4, the producer's and user's accuracies for water, bare land, and vegetation in all four scenarios were greater than 94%. The water and vegetation extraction was improved, followed by that of bare land. The reason for this is that sparse grass can be classified as vegetation and bare land, and it is difficult to accurately determine which type the associated features belong to. The extraction effect of buildings and roads was relatively poor as the resolution of the images was high, and some narrow roads were interspersed among the buildings, making it easy to divide the roads and buildings together during segmentation, causing confusion between the two types of features. From the overall accuracy of the four schemes, the overall accuracy of Fm-RF, Fm-\(k\)NN, and mRMR-SVM were all greater than 95%, which indicates that Fm can better combine and optimize feature subsets and improve the classification ability of the used feature sets. All of the feature selection methods based on Fm could achieve effective surface feature information extraction. Meanwhile, the RFE and LR classification methods did not present high classification accuracy. Wrapper feature selection methods rely on feature models and specific machine learning algorithms, and the optimal feature combinations change as the learners change, which, in some cases, can have detrimental effects. In our experiment, there were negative values in the NDVI and NDWI feature values, and there was a data imbalance; for the embedded LR feature selection method, it is difficult to solve the data imbalance problem. In conclusion, both the single filtering feature selection methods and the combination of the proposed two filtering feature selection methods presented good performance, and filtering feature selection methods can more easily obtain better classification results when performing object-oriented classification.