# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion
### Principal Component Analysis (PCA)  
In order to visualize the lithological variance, this study makes use of PCA- and MNF-transformed bands. The PCA approach yields results that compare favorably with existing geological maps and field data, indicating a very high level of accuracy. Since their eigenvalue and standard deviation statistics likewise show a contrastive broader range and cover maximum variability (Tables 6 and 7), these composites effectively displayed maximal lithological discrimination.  
The combination of the first three main components (PCs 1, 2, and 3) was used in [90] to characterize the lithological variance of volcanic, meta-sedimentary, and sedimentary complexes. Using composites of one to five principal components from the ASTER dataset [91] mapped the host lithologies of igneous and sedimentary origin and delineated the various mineralized zones, such as the alteration zone, manganese, and serpentinite [91; 92; 93]. Similarly, for the Buka Daban mountain in northern Tibet, China, considerable sedimentary succession differentiation was accomplished thanks to different RGB combinations of MNF-transformed VNIR and SWIR bands from ASTER, Landsat-8, and Sentinel-2 satellite  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Eigenvectors** & **Band 1** & **Band 2** & **Band 3** & **Band 4** & **Band 5** & **Band 6** & **Band 7** & **Band 8** & **Band 9** & **Eigenvalues** \\ \hline PC1 & \(-\)0.3069 & \(-\)0.317 & \(-\)0.2935 & \(-\)0.3387 & \(-\)0.3494 & \(-\)0.3491 & \(-\)0.3502 & \(-\)0.3506 & \(-\)0.339 & 0.9813 \\ \hline PC2 & \(-\)0.6513 & \(-\)0.5828 & 0.0871 & 0.3374 & 0.1866 & 0.2215 & 0.1312 & 0.0706 & 0.0931 & 0.5986 \\ \hline PC3 & \(-\)0.0177 & 0.0343 & \(-\)0.9214 & \(-\)0.0771 & 0.1762 & 0.137 & 0.1927 & 0.198 & 0.1321 & 0.3776 \\ \hline PC4 & 0.1369 & 0.0404 & \(-\)0.0732 & 0.3061 & 0.2298 & 0.1811 & 0.1761 & \(-\)0.1423 & \(-\)0.8624 & 0.1179 \\ \hline PC5 & \(-\)0.0149 & \(-\)0.2161 & 0.2 & \(-\)0.6225 & \(-\)0.0066 & \(-\)0.0429 & 0.4178 & 0.529 & \(-\)0.2632 & 0.0508 \\ \hline PC6 & \(-\)0.646 & 0.685 & 0.024 & 0.0601 & \(-\)0.114 & \(-\)0.1117 & \(-\)0.0119 & 0.2484 & \(-\)0.1485 & 0.0197 \\ \hline PC7 & \(-\)0.201 & 0.1887 & 0.1042 & \(-\)0.5265 & 0.463 & 0.4255 & \(-\)0.038 & \(-\)0.4844 & 0.0661 & 0.0135 \\ \hline PC8 & 0.0149 & 0.0161 & \(-\)0.0224 & \(-\)0.0294 & \(-\)0.6907 & 0.7207 & 0.0271 & \(-\)0.0065 & \(-\)0.0315 & 0.0105 \\ \hline PC9 & 0.0666 & \(-\)0.0726 & 0.0032 & \(-\)0.0506 & 0.234 & 0.2505 & \(-\)0.7843 & 0.4863 & \(-\)0.1365 & 0.0101 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical details of eigenvectors of each ASTER band against the individual principal components.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **ASTER SVM** & **ASTER MLC** & **OLI SVM** & **OLI MLC** \\
**Total Ground** & **Total Ground** & **Total Ground** & **Total Ground** \\
**Truth Pixels** & **Truth Pixels** & **Truth Pixels** & **Truth Pixels** \\ \hline \multicolumn{1}{c}{} & & & \\ \hline \multicolumn{1}{c}{} & & & \\ \hline \multicolumn{1}{c}{} & & & \\ \hline \multicolumn{1}{c}{} & & & \\ \hline \multicolumn{1}{c}{} & & & \\ \end{tabular}
\end{table}
Table 5: Total ground truth pixels of each class for MLC and SVM classifiers using OLI and ASTER.  
sensors [94]. In this study, we used PCs 1, 4, 6 and 3, 5, 7 of ASTER and PCs 1, 3, 4 and 2, 4, 7 of OLI as RGBs for lithological classification of the study area. Figures 6 and 7 indicate that the combinations were very helpful for discriminating lithologies.