Hyperspectral vs. multispectral remote sensing: ASTER and PRISMA performance in mapping IOA deposits in the Bafq mining district, Iran

Hyperspectral and multispectral remote sensing are increasingly important in mineral exploration, yet their effectiveness varies with sensor characteristics and image processing strategies. This study compares PRISMA hyperspectral and ASTER multispectral imagery for alteration mineral mapping in a mineralized region. Several processing techniques were applied, including Principal Component Analysis, Mixture Tuned Matched Filtering, Spectral Feature Fitting, false color composites, band ratios, and Minimum Noise Fraction transformation. Field surveys and laboratory spectral measurements were used for validation. PRISMA data show a higher consistency with field and XRD validation results compared to ASTER, confirming its superior capability in hydrothermal alteration mapping. Among the tested methods, Principal Component Analysis, Mixture Tuned Matched Filtering, and Spectral Feature Fitting produced the most accurate and geologically meaningful alteration maps, clearly outperforming conventional band ratios and false color composites. This enhanced performance is linked to improved noise suppression, greater spectral contrast, and more effective separation of mineralogical signals from the geological background. Overall, the study highlights the strong potential of PRISMA hyperspectral imagery for early-stage mineral exploration and recommends future research on combining it with machine learning and deep learning techniques to further improve accuracy and efficiency.