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.

Multispectral and hyperspectral remote sensing represent critical instrumental methodologies that significantly enhance geological mapping and facilitate the identification of mineral resources. Collisional remnants of Himalaya-Hindukush northern Pakistan offer a unique opportunity to explore the hydrothermal deposits hosting economic minerals, inaccessible due to rugged and steep topography. The primary applications of ASTER and ZY1-02D remote sensing data include the extraction of alteration zones to detect economically significant minerals and depicting lithological units. To assess potential mineralization zones in the rugged terrain of northern Pakistan, herein, we implement integrated remote sensing techniques like false color composite, decorrelation stretching, band ratio, machine-learning techniques (maximum likelihood classification, spectral angle mapper, neural net classification, and match filtering), and validation (field observations). The results revealed 11 lithological units and several mineralization zones with key indicator minerals. These zones contained clay minerals, i.e. kaolinite, muscovite/sericite, and propylitic minerals, including calcite and chlorite, indicating copper and iron mineralization. The detected minerals emphasize that hydrothermal alteration in the region, controlled by the hot fluids from the granitic plutons (Tarichmir, Garam Chashma, and Kafiristan) of northwest Pakistan. The acquired mineral signature from remote sensing data was validated with ground truth observations, spectrometry, and petrographic analysis. Furthermore, this study proved the effectiveness of implemented approaches in delineating mineralization zones and discriminating lithological units.