This study was undertaken to evaluate the applicability of hyperspectral remote sensing for the identification and mapping of heavy minerals within the study area. Analysis of EO-1 Hyperion imagery demonstrates its capability to delineate mineralogical variations and estimate relative mineral abundances, thereby supporting geological characterization at a regional scale. The detected mineral assemblages show strong correlation with known lithological units, indicating the reliability of hyperspectral data for preliminary exploration targeting
However, the spatial distribution of identified minerals is influenced by terrain-induced distortions and vegetation cover, which may obscure or modify spectral responses. In addition, classification accuracy is constrained by inherent limitations of Hyperion data, including low signal-to-noise ratio (SNR), spectral smile effects, and dependence on laboratory-based reference spectra from standard spectral libraries that may not fully represent in-situ conditions.
Despite these limitations, hyperspectral sensors—owing to their high spectral resolution—are highly sensitive to diagnostic absorption and reflectance features of minerals. This enables effective discrimination of mineral phases and mapping of their spatial distribution in surface materials, making hyperspectral analysis a valuable tool for reconnaissance-level exploration.
Based on the integration of hyperspectral results with geospatial datasets and the presence of active mining operations in the vicinity, several prospective zones have been identified. It is therefore recommended to undertake systematic ground validation, including field mapping, spectral measurements, and targeted sampling, to verify mineral occurrences and refine exploration targets for subsequent detailed investigation and resource evaluation.