# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 1 Introduction  
One of the significant components in geology is the mapping of the various units of rocks exposed on Earth's surface, which is called lithological mapping. A lithological map by itself provides a great amount of enlightenment on rock characteristics and is very vital for economic geology. Lithological unit mapping can provide details on the spatial distribution of various rock units and their structural occurrences, which can be used to analyze and map potential mineralization zones [1, 2, 3, 4, 5, 6, 7]. Remote sensing (RS) has gained popularity as a cost-effective and effective tool for regional lithological mapping, particularly in arid and semi-arid environments [8, 9, 10, 11]. For many years, optical imagery collected by airborne and spaceborne sensors has been used extensively in mineral and lithological exploration. Different rock types can display unique spectral absorption or reflection patterns in the pertinent electromagnetic spectrum, depending on their mineral compositions [12]. In the near-infrared (NIR), short-wave infrared (SWIR), and thermal infrared (TIR) spectral ranges, several minerals exhibit discriminative behavior [12, 13, 14]. Remote sensing sensors capture electromagnetic (EM) spectra of various wavelengths that are reflected or absorbed in order to identify materials based on their usual response as a result of varied chemical and physical attributes. Therefore, researchers use the RS data spectral bands to study and identify various lithologies using a variety of methodologies. Large-scale mapping may be performed affordably with multispectral satellite sensors like Landsat 8 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER). ASTER, launched in 1999 on the Terra platform, has three VNIR, six SWIR, and five TIR spectral bands with spatial resolutions of 15, 30, and 90 m, respectively [15]. On the other hand, Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 m for Bands 1 to 7 and 9. Aerosol and coastal research benefit from the usage of the new band 1 (ultra-blue). The new band 9 is helpful in detecting cirrus clouds. In the literature, multispectral Landsat, Sentinel 2, and ASTER images have been widely utilized for geological structure mapping, lithology discrimination, geohazard identification and mitigation, geomorphology and landform processes, and mineral exploration [16; 17; 18; 19; 20; 21]. Digital image processing is the field of RS defined as the creation of modified images that contain more information to support the visual interpretation of features by manipulating remotely sensed data. Due to its higher spectral resolution in the SWIR and TIR region than other multispectral data, ASTER has a greater potential to perform semi-quantitative mineral mapping. This has allowed it to become the most popular form of imaging in geological research, particularly in hydrothermal alteration and lithological unit differentiation [22; 23; 24; 20]. Even though the SWIR sensor of ASTER has not been active since 2008, about 4 million images with nearly worldwide land coverage have been recorded [25] with SWIR bands and are available for use. Principal component analysis (PCA), band ratios, MNF, and decorrelation stretching have all been widely utilized to map various lithologies, mostly using ASTER [26; 27; 24; 25; 26; 2], Landsat 8 OLI [2; 3; 7], and Sentinel-2 [4; 5; 6] or their fused combination [3; 6; 7; 24; 25; 26; 27; 28]. Machine learning algorithms can be grouped into four main categories: supervised, unsupervised, self-supervised, and reinforcement learning algorithms [29]. Supervised algorithms are utilized to forecast the outputs that fit the provided targets. Unsupervised techniques including clustering, PCA, and decorrelation stretching (DS) are used in the preprocessing stage before the ML algorithm to improve the accuracy of the data annotation [3; 30].