Assessing Landsat-9 in Identifying Lithology, Using a Hybrid Metric-Learning and SVM Method Against Baseline Algorithms: A Case Study of the West African Craton

The West African Craton hosts economic minerals such as diamonds, gold, and bauxite. Yet, it remains underexplored as poor socio-political and environmental conditions limit traditional exploration in the area. This study assesses the performance of the Landsat-9 OLI-2/TIR-2 in mapping lithologies and highlighting potential metallogenic points using remote sensing techniques. Specifically, we perform classification tasks on preprocessed Landsat-9 dataset to classify the lithological features using a hybrid machine learning (ML) module integrating Local Fisher Discriminant Analysis (LFDA) and Support Vector Machine (SVM) against three (3) baseline ML algorithms. The aim is to enhance computing proficiency and improve classification precision. The accuracy results show that the proposed LFDA and SVM algorithm performs better than Random Forest (RF) and KNN. The results distinguish nine lithological units with a 0.9992 overall accuracy. Additionally, Landsat-9 OLI-2/TIR-2 proves efficient in attaining high accuracy in conducting geological exploration work in terrains under unfavorable conditions.