Lithological Classification Using ZY1-02D Hyperspectral Data by Means of Machine Learning and Deep Learning Methods in the Kohat–Pothohar Plateau, Khyber Pakhtunkhwa, Pakistan

Waqar Ahmad, Lei Liu, Zhenhua Guo, Yasir Shaheen Khalil, Nazir Ul Islam, Fakhrul Islam

Lithological mapping using satellite images, particularly hyperspectral data, helps in effectively defining the best initial targets for regional exploration. In this study, ZY1-02D hyperspectral image (HSI) data with moderate spectral and very high spatial resolution were employed for lithological mapping using spectral indices along with support vector machine (SVM) machine learning and spatial–spectral transformer (SSTF) deep learning methods in the Kohat–Pothohar Plateau at the eastern edge of the Main Boundary Thrust (MBT) in Pakistan. The research was accomplished using spectral profiles of minerals accompanied by false color composite (FCC), principal component analysis (PCA), SVM, and SSTF methods for classifying the main lithological units. The lithological discrimination map derived from the ZY1-02D data matched well with the known deposits and field inspections. The principal component analysis (PCA) obtained the highest eigenvalues and provided a significant discrimination of lithologies, particularly with hyperspectral data. The results revealed lithological units, three of which contained limestone and gypsum, while other lithological units were defined as sandstone, clay, and conglomerates. Field investigation and laboratory sample analysis through X-ray diffraction (XRD), photomicrographs, and spectral analysis confirmed the occurrence of limestone, gypsum, and sandstone, which are useful in identifying lithological units in the study area. This study will assist in more accurate geological discrimination and play a vital role in identifying oil and gas reservoirs, coal, gypsum, uranium, salt, and limestone deposits. Furthermore, the results of the SVM and SSTF techniques were quantitatively compared with the geological boundaries mapped in the field, showing an accuracy of nearly 89.7% and 92.1%, respectively. Overall, the methodology adopted showed great performance and strong potential for mapping alteration areas and lithological discriminations applied on the ZY1-02D hyperspectral data.