# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 6 Conclusions  
Remote sensing techniques and two popular supervised machine learning approaches (SVM and MLC) were utilized on multispectral data of ASTER and Landsat 8 to compare their efficiency in differentiating lithological units in the Khyber region. The remote sensing methods used in the study included principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites. Training samples for MLA were gathered on the basis of previously published geological maps and field data. The PCA and false color composites of ASTER were better than those of OLI for lithological discrimination of the study area. The lineament analysis of the research region showed that major and minor structures could be easily found in wide expanses of land and that this is, thus, a valuable technique for quickly obtaining an overview of the structural features of the area. The results show that the geological formations are mapped with >70% OA with the SVM approach using ASTER. The calculated accuracy assessment of SVM and MLC using a confusion matrix revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. Overall, the results indicate that ASTER imagery has better potential for lithological discrimination in the study area and that the optimal approach in this case is SVM. A more detailed map of nine geological formations discriminating various lithological units was produced using ASTER and OLI satellite RS data. The study demonstrated that lithological mapping in any dry to semi-arid region could effectively be performed at a lower computing cost using such techniques on multispectral data.  
Figure 17: (**a**) Study area geological map with extracted lineaments in white color. (**b**) The lithological map generated from SVM classified using ASTER, with extracted lineaments as orange color lines.  
From the comparisons and discussion, the following conclusions are reached:  
1. ASTER has better results than OLI for using remote sensing and MLC and SVM machine learning techniques for the lithological discrimination of the Khyber range.
2. In semi-arid and dry regions like the Khyber range, SVM outperforms MLC in lithological classification.  
Conceptualization: S.A. and H.L.; methodology: S.A., H.L. and A.A.; software: S.A. and H.L.; validation, S.A.; formal analysis: S.A. and H.L.; investigation: S.A. and A.A.; data processing: S.A., H.L. and J.H.; writing--original draft preparation: S.A.; writing--review and editing: H.L., A.A. and J.H.H. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (No. 92162103), the Natural Science Foundation of Hunan Province (No. 2022 JJ30699, No. 2023 JJ10064), and the Science and Technology Innovation Program of Hunan Province (No. 2021 RC4055, No. 2022 RC1182).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.