# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion
### Machine Learning Algorithms (MLAs)  
The SVM and MLC approach classifies the studied lithological units in the study region with results very much matching the geological map, with some exceptions. In general, the ASTER sensor outperformed the OLI sensor, and the SVM outperformed the MLC for lithological classification of the study area. Additionally, the synthetic character of the  
Figure 14: (**a**) Oriented photograph showing dolerite intrusion in the Khyber limestone. These intrusions exist in the form of dikes and sills. (**b**) Field photograph showing contact between Khyber limestone and Shagai formation. (**c**) A distant view of the contacts between the outcrops of Landi Kotal slate, Khyber limestone, and Shagai formation in the study area. (**d**) Figure showing the sample from Ghundai Sar formation in the study area.  
borders between lithological units and the generalization used in the creation of geological maps both have an impact on the overall accuracy attained. The SVM well discriminated the Murree formation in both ASTER and OLI, represented in dark greyish and greenish color, respectively, in the resultant maps (Figure 12a,b). The SVM also well discriminated the Ghundai Sar formation in both ASTER and OLI, represented in dark greyish and greenish color, respectively, in the output SVM classification maps. The SVM classification for Khyber limestone using ASTER is shown as reddish, Shagai formation shale as dark green, and river as yellow color in Figure 12a. The SVM classification using OLI for the Ghundai Sar formation is shown in orchid purple, Khyber limestone in mostly sky-blue color, Shagai formation shale in navy-blue color, bazar limestone in blue color, and river in magenta color (Figure 12b). The MLC classification results for the Murree formation using ASTER are shown as royal purple color, Ghundai Sar formation as pink color, and Khyber limestone as mostly light green color in Figure 13a. The MLC classification using OLI for the Murree formation is shown in bright pink color and the Ghundai Sar formation in sea green color in Figure 13b. A total of nine lithological units were mapped in this research. A field visit was carried out to verify the classified lithologies on the ground, which indicated that the results of this study are mostly in accordance with the field features/lithologies (Figure 14). ML maps showed that the following lithost stratigraphic units were detected in the field: Khyber limestone, Shagai formation, Landi Kotal slate, Lowara Mena complex. Khyber limestone covered the majority of the outcrops in the area that was mapped. The field validation and sample petrographic results also indicate a good correlation with the ML-produced lithological maps as shown in Figures 14 and 15. The mineral compositions using petrographic studies of the samples from different lithologies (Landi Kotal slate, Shagai formation, Khyber limestone) of the study area are shown in Figure 15.  
matrix. (**d**) Sample collected from Khyber limestone, showing fractured spray to micritic limestone with recrystallized matrix and locally well-developed cross-hatched calcite crystals. The fracture in the rock is filled with opaque minerals that crosscut the main fabric of the recrystallized limestone.