Machine Learning Algorithms for Lithological Mapping Using Landsat 9 Data in Central Western Highlands of Yemen

This research designed the lithological units of the Central Western Highlands of Yemen (encompassing parts of Dhamar, Raymah, Sana’a, and northern Ibb) using Landsat 9 imagery. The area's complex geological features, characterized by units of the Yemen Volcanic Group from the Tertiary and Quaternary eras, Tertiary granite intrusions, and limestone, sandstone, metamorphic rocks, and Quaternary deposits, pose challenges for traditional field mapping techniques. By leveraging the spectral resolution of Landsat 9, this study aims to achieve accurate classification and mapping of lithological units. ENVI 5.6 software was used for image processing, applying a supervised classification approach represented by the two most common methods: Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC), based on training samples for each lithological class. The accuracy assessment of the classification was validated through an error matrix. The overall accuracy of SVM reached 85.3% with a Kappa coefficient of 0.8, while the overall accuracy of MLC reached 83.3% with a Kappa coefficient of 0.8, indicating a high degree of consistency and reliability in the classification process. This signifies a highly reliable classification outcome. The findings of this study highlight the significant advantages of utilizing Landsat 9 for detailed geological mapping of complex terrains, demonstrating a notable improvement in efficiency and accuracy over traditional methodologies. It can be relied upon to classify lithological units in other areas.