# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 3 Material and Methods
### Feature Extraction  
PCA is a convenient and effective multivariate statistical method for removing the redundancy and irradiance effects of satellite images [10; 43]. It has been widely used with multispectral remote sensing data to identify lithologic units. Here, the first three components contained 98.69% information (Table 3). The higher PC components revealed substantial information related to the distribution of lithological units. Thus, the first seven PCs were selected in this study. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sentinel-2B**} & \multicolumn{4}{c}{**Landsat-8**} & \multicolumn{2}{c}{**ASTER**} \\ \hline
**Band** & **Central Wavelength (nm)** & \begin{tabular}{c} Spatial \\ Resolution (nm) \\ \end{tabular} & **Band** & **Central Wavelength (nm)** & \
\begin{tabular}{c} Spatial \\ Resolution (nm) \\ \end{tabular} & **Band** & **Central Wavelength (nm)** &
\begin{tabular}{c} Spatial \\ Resolution (nm) \\ \end{tabular} \\ \hline B1 & 442.3 & 60 & B1 & 443 & & B1 & 556 & 15 \\ B2 & 492.1 & & B2 & 482.5 & & B2 & 661 & 661 \\ B3 & 559 & 10 & B3 & 562.5 & & B3N & 807 & \\ B4 & 665 & & B4 & 655 & & 30 & B3B & 807 & \\ B5 & 703.8 & & B5 & 865 & & B4 & 1656 & 30 \\ B6 & 739.1 & 20 & B6 & 1610 & & B5 & 2167 & \\ B7 & 779.7 & & B7 & 2200 & & B6 & 2209 & \\ B8 & 833 & 10 & B8 & 640 & 15 & B7 & 2262 & \\ B8a & 864 & 20 & B9 & 1375 & & B8 & 2336 & \\ B9 & 943.2 & & B10 & 10,900 & & B9 & 2400 & \\ B10 & 1376.9 & & B11 & 12,000 & & B10 & 8291 & 90 \\ B11 & 1610.4 & & & & & B11 & 8634 & \\ B12 & 2185.7 & & & & & B12 & 9075 & \\  & & & & & & B13 & 10,657 & \\  & & & & & & B14 & 11,318 & \\ \hline \end{tabular}
\end{table} 
Table 2: Description of Sentinel-2B, Landsat 8 and ASTER data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Eigenvalue** & **Percentage** & \multicolumn{2}{c}{**Eigenvalue**} & **Percentage** & \multicolumn{2}{c}{**Eigenvalue**} & **Percentage** \\ \hline pc1 & 4,485,100.755 & 93.42\% & MNF1 & 49.297124 & 34.19\% & IC1 & 1 & 10.00\% \\ pc2 & 149,135.8372 & 3.11\% & MNF2 & 43.139567 & 29.71\% & IC2 & 1 & 10.00\% \\ pc3 & 103,718.3366 & 2.16\% & MNF3 & 17.33235 & 11.86\% & IC3 & 1 & 10.00\% \\ pc4 & 31,448.10436 & 0.66\% & MNF4 & 12.900324 & 8.76\% & IC4 & 1 & 10.00\% \\ pc5 & 14,635.23885 & 0.30\% & MNF5 & 9.778669 & 6.60\% & IC5 & 1 & 10.00\% \\ pc6 & 10,136.38866 & 0.21\% & MNF6 & 3.930291 & 2.63\% & IC6 & 1 & 10.00\% \\ pc7 & 2356.034608 & 0.05\% & MNF7 & 2.299615 & 1.53\% & IC7 & 1 & 10.00\% \\ pc8 & 1785.780308 & 0.04\% & MNF8 & 2.100346 & 1.39\% & IC8 & 1 & 10.00\% \\ pc9 & 1383.225922 & 0.03\% & MNF9 & 1.75929 & 1.16\% & IC9 & 1 & 10.00\% \\ pc10 & 1199.32786 & 0.02\% & MNF10 & 1.649774 & 1.08\% & IC10 & 1 & 10.00\% \\ \hline \end{tabular}
\end{table} 
Table 3: Percentage of information contained in each PCA, MNF, ICA components. The MNF proposed by [PERSON] et al. [44] is a well-known data dimension and noise reduction technique. It consists of two cascaded PCA rotations. The first PC rotation performs noise whitening and results in transformed data in which the noise has a unit variance but no band-to-band correlation. The second rotation uses PCs derived from the original image data after they have been whitened by the first rotation and rescaled by noise standard deviation. Similar to the PCA output bands, the first MNF band contains the highest variance, and the second MNF band contains the second-highest variance [45; 46]. As the last three components that were included are strongly affected by noise, only the first seven MNF components were selected for lithological mapping (Table 3). ICA is used on multispectral or hyperspectral datasets to transform a set of mixed, random signals into mutually independent components. It is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. Compared with PCA and MNF, the ICA transform uses higher-order statistics for signal separation and feature extraction [47; 48].