# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 4 Results
### Comparison between Lithological Maps of Different Optimisation Methods  
The lithological mapping results of the PCA, ICA, MNF and GLCM datasets are shown in Figure 6. Only the PCA, ICA and MNF datasets yielded good results and correctly matched the visual interpretation results of the study area. Compared with the results of Sentinel-2, less noise was involved in the classification results of PCA, ICA and MNF. The classification results of the GLCM datasets had the worst performance. For the classification results of each rock unit, the four datasets had low degrees of recognition of \(Q_{1q}\) and \(E_{1-21}\). Given the similar lithological composition, \(Q_{1q}\) was easily misclassified with \(Q_{3}^{pl}\) and \(Q_{4}^{pl}\). Figure 5: Lithological mapping results of different multispectral data classified by RF. (**a**) Sentinel-2, (**b**) Landsat OIL; (**c**) ASTER. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Sentinel-2**} & \multicolumn{3}{c}{**ASTER**} & \multicolumn{3}{c}{**Landsat-OIL**} \\ \cline{2-10}
**Lithological Classes** & \multicolumn{3}{c}{**Overall Accuracy: 60.71\%**} & \multicolumn{3}{c}{**Overall Accuracy: 55.47\%**} & \multicolumn{3}{c}{**Overall Accuracy: 55.94\%**} \\  & \multicolumn{3}{c}{**Kappa Coefficient: 0.57**} & \multicolumn{3}{c}{**Kappa Coefficient: 0.52**} & \multicolumn{3}{c}{**Kappa Coefficient: 0.52**} \\ \cline{2-10}  & **Producer’s** & **User’s** & **Average** & **Producer’s** & **User’s** & **Average** & **Producer’s** & **User’s** & **Average** \\ \hline Q\({}_{1q}^{pl}\) & 69.24 & 70.92 & 70.08 & 60.92 & 63.4 & 70.08 & 43.97 & 43.48 & 43.73 \\ Q\({}_{2q}^{pl}\) & 76.36 & 85.77 & 81.07 & 81.42 & 83.83 & 81.07 & 32.94 & 24.5 & 28.72 \\ Q\({}_{1q}\) & 45.12 & 40.53 & 42.83 & 31.34 & 47.81 & 42.83 & 57.75 & 50.41 & 54.08 \\ N\({}_{2q}\) & 21.9 & 100 & 60.95 & 18.05 & 77.08 & 60.95 & 48.39 & 73.33 & 60.86 \\ E\({}_{3q}\) & 87.97 & 53.98 & 70.98 & 92.33 & 46.16 & 70.98 & 49.75 & 11.3 & 30.53 \\ E\({}_{1-21}\) & 48.87 & 31.76 & 40.32 & 47.33 & 41.4 & 40.32 & 55.32 & 54.35 & 54.84 \\ K\({}_{m}\) & 56.84 & 39.45 & 48.15 & 57.86 & 34.81 & 48.15 & 55.49 & 35.71 & 45.60 \\ Z\({}_{2q}^{2}\) & 42.17 & 80 & 61.09 & 58.05 & 47.27 & 61.09 & 37.7 & 58.24 & 47.97 \\ Z\({}_{1-4h}^{1n}\) & 79.76 & 69.57 & 74.67 & 61.7 & 58.39 & 74.67 & 35.42 & 30.08 & 32.75 \\ Z\({}_{2q}^{2}\) & 58.39 & 69.12 & 63.76 & 36.75 & 57.25 & 63.76 & 48.74 & 51.97 & 50.36 \\ J\({}_{3h}\) & 49.76 & 74.32 & 62.04 & 53.59 & 72.26 & 62.04 & 37.04 & 58.2 & 47.62 \\ J\({}_{1-2}\) & 70.99 & 89.53 & 80.26 & 47.83 & 72.79 & 80.26 & 13.92 & 46.03 & 29.98 \\ C\({}^{+}\)\({}_{3 hh}\) & 66.78 & 64.16 & 65.47 & 32.94 & 73.4 & 65.47 & 28.42 & 40.87 & 34.65 \\ C\({}^{+}\)\({}_{3 hh}\) & 42.38 & 73.55 & 57.97 & 76.68 & 55.68 & 57.97 & 52.46 & 65.88 & 59.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification accuracy and kappa coefficient of Sentinel-2, ASTER and Landsat-OIL. The classification overall accuracies and kappa coefficients of different datasets are shown in Table 6. Compared with the four optimal datasets, ICA attained the highest overall accuracy and kappa coefficient, at 65.21% and 0.62, respectively. GLCM obtained the lowest overall accuracy and kappa coefficient, with values of 58.87% and 0.56, respectively. The overall accuracies and kappa coefficients of PCA, ICA and MNF were higher than those of Sentinel-2. \(Q_{4}^{pl}\) and \(Z_{dk}^{2}\) had the highest average classification accuracy in the PCA datasets; \(J_{1-2}\) and \(C_{3 zh}^{c}\) had the highest average classification accuracy in the MNF datasets; and \(C_{3 zh}^{b}\) and \(Q_{3}^{pl}\) had the highest classification accuracy in the ICA datasets.