## 5 Conclusions  
In this paper, an algorithm combining the Fisher Score and mRMR algorithms is proposed to address the problem of high dimensionality of the feature space in object-oriented classification. Although Fisher Score and mRMR feature selection methods have good applicability in feature screening, a single method cannot take into account the redundancy between features and the correlation between features and categories at the same time. The Fisher Score algorithm does not take into account the redundancy between features, and the mRMR algorithm cannot reflect the differences in the role of different features in classification. The combination of the Relief and mRMR algorithms can effectively make up for their shortcomings. After the experiments involving four different machine learning classification methods, the overall accuracy of Fm combined with RF and KNN is better than that of RF and CART.  
Through a comparative test of four kinds of classifiers, we determined that: (1) Feature selection can allow for the elimination of redundant features, and high classification  
Figure 8: Fm classification image with different classifiers: (a) CART; (b) RF; (c) KNN; (d) SVM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CART** & **RF** & **KNN** & **SVM** \\ \hline Overall accuracy & 88.67\% & 92.04\% & 91.08\% & 88.68\% \\ Kappa & 0.8545 & 0.8979 & 0.8852 & 0.8546 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall accuracy and kappa coefficient of Fm with different classifiers.  
accuracy can still be achieved when using a small number of features. Accordingly, filtered feature selection methods were found to perform better than wrapped and embedded feature selection methods. (2) Two classifiers--RF and SVM--exhibited better stability than the other two classifiers as the number of features increased during the experiment. (3) In this study, the proposed Fm feature selection method was used in the classification experiment and showed the best performance when used with the RF and \(k\)NN classifiers, allowing for better optimization of the feature set. The final classification accuracy and efficiency were improved obviously by using the Fm feature subset. The overall accuracy of the Fm-RF and Fm-\(k\)NN approaches reached 95.18% and 96.14%, respectively. The kappa coefficient reached 0.939 and 0.951, respectively. Except for the mapping accuracy of Fm-RF construction land, the mapping accuracy and user accuracy of Fm-RF and Fm-KNN both reached more than 91%.  
Conceptualization, Y.L. and C.L.; methodology, C.L.; software, H.F.; validation, X.F. and C.X.; formal analysis, L.X.; investigation, M.L.; data curation, X.F.; writing--original draft preparation, C.L.; writing--review and editing, Y.L.; supervision, L.X.; project administration, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Project of High Resolution Earth Observation System of China (no. GFZX0404130304); the Open Fund of Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology (no. E22201); the Agricultural Science and Technology Innovation Program (ASTIP no. CAAS-ZDRW202201); a grant from the State Key Laboratory of Resources and Environmental Information System; and the Innovation Capability Improvement Project of Scientific and Technological Small and Medium-Sized Enterprises in Shandong Province of China (no. 2021 TSGC1056).  
Not applicable.  
Data Availability StatementData and code from this research will be available from the authors upon request.  
The authors sincerely thank the comments from anonymous reviewers and members of the editorial team.  
The authors declare no conflict of interest.