by Kamta Touoyem Guy Olivier¹*, Jacques Matanga¹, Kabiena Ivan Basile¹, Maka Maka Ebenezer¹, Malong Yannick¹, Ngounou Gamon Christian²
1 Computer Engineering, Data Science and Artificial Intelligence Laboratory, National Polytechnic School of Douala, Cameroon
2 Electrical engineering Department, National School of Technical Education, Cameroon
*Corresponding author: [email protected]
Received: 11.01.2025 Accepted: 26.05.2025 Published online: 28.08.2025
| The identification and characterization of strategic mineral resources remain a critical challenge for developing countries such as Cameroon, where large portions of the territory are still insufficiently explored. This situation is particularly evident in the northern region, which hosts significant occurrences of carbonate minerals (e.g., calcite, dolomite) and silicate minerals (e.g., quartz, feldspars), both of which are of considerable importance for industrial applications. This study evaluated the effectiveness of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite imagery in combination with the Spectral Angle Mapper (SAM) algorithm for detecting and mapping carbonate and silicate minerals in Northern Cameroon. The supervised SAM classifier was calibrated using spectral signatures from the ENVI 5.6 spectral library. The results confirmed that the method successfully identified and spatially delineated calcite, albite, and magnetite within the selected regions. These findings provide a valuable contribution to mineral exploration by improving knowledge of regional mineral distribution while simultaneously reducing the time and cost associated with conventional field-based surveys. |
