Hyperspectral remote sensing offers exceptional spectral detail for identifying minerals, but its relatively coarse spatial resolution often limits its use in geological studies. The PRISMA satellite provides 30 m hyperspectral (VNIR–SWIR) data together with a 5 m panchromatic band, creating the possibility of enhancing spatial detail through image fusion. In this study, we applied two established pansharpening methods—Gram–Schmidt (GS) and Principal Component Analysis (PCA)—to PRISMA data from the Kuh-e-Janja porphyry copper deposit in southeastern Iran. Pre-processing included atmospheric correction, removal of water vapor– affected bands, and Minimum Noise Fraction (MNF) transformation. The hyperspectral cube was then fused with the 5 m PAN band to produce sharpened datasets at 5 m ground sampling distance (GSD). Visual inspection showed that both approaches improved spatial clarity, allowing finer recognition of lithological boundaries, alteration halos, drill sites, roads, and structural features that were not easily visible in the native 30 m imagery. Among the two, GS produced sharper edges and maintained more accurate mineralogical color signatures compared with PCA. Quantitative evaluation across 163 bands supported this result, with GS outperforming PCA in all statistical measures, including SAM, RMSE, ERGAS, CC, UIQI, and PSNR. The generation of 5 m hyperspectral datasets demonstrates the value of combining rich spectral information with fine-scale spatial detail. Such fused imagery provides reliable input for advanced classification techniques (e.g., SAM, SVM, OBIA) and offers a practical framework for mineral exploration, particularly in arid and structurally complex terrains where subtle geological variations are critical to detection.