# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Enhancements** & **PCA** & **MNF** \\ \hline The first three components & 35.68\% & 44.57\% \\ The first five components & 53.54\% & 59.60\% \\ The first seven components & 62.63\% & 64.34\% \\ All components & 64.72\% & 65.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Overall accuracies of different PCA and MNF component datasets classified by RF. when other datasets were added. In a previous study [16; 62], the band with the highest standard deviation was consistently selected to extract textural features. In this study, all bands from Sentinel-2 data were used to extract textural features. This approach has rarely been mentioned in previous studies. The scheme adopted in this study performed better than when combining only the textural features extracted from the highest deviation band (67.92%). A suitable texture window size is important in lithological mapping. Figure 12 presents the change in the rock boundary, in which the window size condition was set from 3 \(\times\) 3 to 11 \(\times\) 11. After adjusting the texture window from 3 \(\times\) 3 to 9 \(\times\) 9, the boundary between \(Q_{pl}^{3}\) and \(z_{dk}^{1a}\) was enhanced. However, when the window size was increased from 9 \(\times\) 9 to 11 \(\times\) 11, the changes in the boundary were negligible. In terms of integrating with other datasets, the 9 \(\times\) 9 datasets presented the highest overall accuracy and kappa coefficient. The classification accuracy increased to 75.75% when the five datasets of Sentinel-2, PCA, ICA, MNF and GLCM were combined, and the improvement was approximately 15% higher than the results obtained by using Sentinel-2 only. Therefore, the more useful the information extracted from the satellite data is, the better the performance of lithological mapping. Then, the same approach was applied to Landsat-8 OLI and ASTER, and their classification accuracies improved to 70.92% and 63.02%, respectively. In the processing, the threshold of weight values needs to be set carefully. In this study, if the threshold was larger than 0.01, then some useful information was removed from the original datasets. If the threshold was lower than 0, the redundant information could not  
Figure 12: Partial magnification of lithological mapping results with different window sizes. **(a)** Visual interpretation results; **(b)** RF of all features with 3 \(\times\) 3 datasets; **(c)** RF of all features with 5 \(\times\) 5 datasets; **(d)** RF of all features with 7 \(\times\) 7 datasets; **(e)** RF of all features with 9 \(\times\) 9 datasets; **(f)** RF of all features with 11 \(\times\) 11. The obvious change area of rock units were outlined by red boxes. be eliminated. Thus, the purpose of improving the feature selection efficiency could not be achieved. Feature selection and optimisation are the basis of accurate classification. The hybrid Relieff-RF model can be used to optimise datasets, effectively reduce redundancy and improve operation efficiency and classification accuracy. The numbers of the optimisation datasets (only 20 bands) were only one-sixth of the combination datasets, whereas the overall accuracy and kappa coefficient values were better than those of all feature combination datasets. Therefore, in the processing stage of feature selection, large amounts of information can be integrated into optimisation datasets, subsequently enhancing the total accuracy. Some natural and human factors, such as erosion and buildings, affect the accuracy of classification, as shown in Figure 13. Currently, no effective approach can automatically eliminate this impact. The datasets can only be modified by human-computer interactive interpretation.