# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 4 Results
### SVM and MLC Classification Results  
To optimize the lithological classification of the study area using remote sensing data, we experimented with training SVM and MLC with different combinations of input data selected from ASTER and OLI images. ROIs covering different lithological units were selected using the field data, literature, and the preceding geological maps. The SVM and MLC image classification results are shown in Figures 12 and 13, respectively. Following the algorithm classification results, the classification's accuracy is evaluated by creating a confusion matrix and pixel-by-pixel comparing the output with the geological map and field data. Reviewing the confusion matrix of SVM reveals kappa values of 0.7 and 0.5036 and overall accuracy rates of 74.8419% and 58.4833% for ASTER and OLI, respectively. Reviewing the confusion matrix of MLC reveals kappa values of 0.6764 and 0.5398 and overall accuracy rates of 72.1217% and 60.0257% for ASTER and OLI, respectively. With a polynomial kernel of degree 1 and a cost parameter of 0.02, SVM performs best at a gamma value of 1/6. Both algorithms received identical training samples, which were gathered after the data had been preprocessed. Good results were reported for both algorithms, with training and validation accuracy of over 70% for ASTER. The PA and UA of MLC and SVM are shown in Tables 1-4 and total ground truth pixels are shown in Table 5 for each lithological unit mapped, but unclassified and water values have been removed from these for a better understanding of lithological units. Using OLI, Ghundai Sar (GS) and Khyber limestone (K) had the highest UA for MLC classification, and the Ali Masjid formation (AM) had the highest UA for SVM classification. The UA of almost all formations was higher in SVM and MLC classifications using ASTER than those using OLI. Figure 11: Carbonate ratio = band 13/band 14. The bright pixels represent quartz dominance. Figure 12: (**a**) ASTER SVM classification showing mapping results for different lithologies: Murree formation in dark greyish, Ghundai Sar formation in brownish color, Khyber limestone as mostly red, Shagai formation shale as dark green, river as yellow. (**b**) OLI SVM classification results showing different lithologies mapped: Murree formation in greenish color, Ghundai Sar formation in orchid purple, Khyber limestone as mostly sky-blue color, Shagai formation shale as navy-blue color, Bazar limestone as blue color, river as magenta color. The boundaries of geological formations (Figure 2) are marked in white color in these figures to show SVM classification in relation to the geological map. Figure 13: (**a**) ASTER MLC classification results, showing different lithologies mapped; Murree formation in royal purple color, Ghundai Sar formation in pink color, Khyber limestone as mostly light green color. (**b**) OLI MLC classification results showing different lithologies mapped: Murree formation in bright pink color, Ghundai Sar formation in sea green color. The boundaries of geological formations (Figure 2) are marked in white color in these figures to show SVM classification in relation to the geological map.