# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 5 Discussion  
This research demonstrated the performance of Sentinel-2 data for the lithological mapping of a study area in Xitieshan, Northwest China. Sentinel-2 performed better than Landsat-8 OLI and ASTER. These results are consistent with the findings in previous research [6; 12]. Given the similar or even higher spatial and spectral characteristics in the VNIR part, Sentinel-2 has a greater potential for lithological mapping than Landsat-8 OLI, especially for ferric rocks [9]. In this study, the classification accuracies of Sentinel-2 for the 11 rock units were better than those of Landsat-8 OLI, especially for Q\({}_{3}\)P\({}^{1}\), E\({}_{3g}\), Z\({}^{2}\)  
Figure 10: OOB errors calculated by RF adding features one by one according to the importance. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Selected Features**} \\ \hline \multirow{4}{*}{Optimal datasets} & (1) MNF2, (2) MNF3, (3) mean of band 1, (4) ICA3, \\  & (5) ICA2, (6) mean of band 2, (7) MNF4, (8) IC5, (9) mean \\ \cline{1-1}  & of band 9, (10) mean of band3, (11) mean of band 4, \\ \cline{1-1}  & (12) mean of band 8, (13) PCA5, (14) mean of band 6, \\ \cline{1-1}  & (15) mean of band 7, (16) PC6, (17) mean of band 12, \\ \cline{1-1}  & (18) mean of band 5, (19) ICA1, (20) homogeneity of band 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The optimal datasets selected by Relief-RF model. Figure 9: The order of weight of each feature calculated by RF algorithm (top30). and J1-2. Sentinel-2 performed better in mapping, whereas ASTER performed better in classifying rocks containing carbonate, hydrate and hydroxyl-bearing sulphate, silicate and other minerals with SWIR absorption features [61], such as Q3P1, E3g, Kqn, \(Z^{2}_{\text{dk}}\) and Cb3 zh (Table 1). Image enhancement techniques, such as PCA, ICA and MNF, could highlight the lithological units and improve the quality of input datasets. After MNF transformation, the geological information was mainly concentrated into two to five components. The false colour composite image with R(MNF4)-G(MNF3)-B(MNF2) allowed the boundary information of \(Z^{2}_{\text{dk}}\), \(Z^{1b}_{\text{dk}}\) and \(Z^{1a}_{\text{dk}}\) to become more obvious than the true colour image of Sentinel-2 (Figure 11). In lithological mapping, image enhancement techniques can increase the quality of the input dataset and improve classification accuracy. Amongst the techniques used in this study, the ICA datasets obtained the highest lithological mapping accuracy owing to their higher-order statistics for signal separation and feature extraction and all ICA components were preserved. For PCA and MNF, which are well-known data dimension and noise reduction techniques, the higher-order components contained more information, but the lower-order components also influenced the accuracy of lithological classification (Table 9). The lithological mapping results indicate that the first three components of PCA and MNF, which contained 98.69% and 75.76% of the information, respectively, performed poorly in the lithological classification task. After adding the first seven components, the classification accuracies of the PCA and MNF datasets significantly increased to 26.95% and 19.77%, respectively. The highest classification accuracy was achieved when all components were used in PCA and MNF. This result indicates that the rankings of PCA and MNF are insufficient in determining their importance in lithology classification. The lower-order components also contained only a few useful pieces of information for lithological mapping. The textural dataset, when used independently, obtained the lowest classification accuracy (overall accuracy of 58.87%); however, the lithological mapping accuracy improved  
Figure 11: (**a**) Sentinel 2A (4, 3, 2) true colour image. (**b**) Image enhancement applied on a Sentinel 2A in RGB with R (MNF4) G (MNF3) B (MNF2).