Spectral classification of diverse lithologies using multi- and hyperspectral satellite data: a comparative study

Accurate lithological mapping requires selecting the appropriate remote sensing data and classification methods. This study evaluates the performance of four satellite datasets—Landsat 8 OLI, Sentinel-2A, ASTER, and Hyperion EO-1—using three spectral classification techniques: Matched Filtering (MF), Spectral Angle Mapper (SAM), and Spectral Information Divergence (SID). The study area is located between the Zara and Koyulhisar districts in eastern Türkiye and comprises diverse lithological units. A total of 49 rock samples collected in the field were used for validation. The results indicate that MF consistently outperformed the other methods, achieving the highest accuracy with Landsat 8 (Kappa = 94.2%). ASTER data demonstrated strong capability in distinguishing lithologies with subtle spectral differences, particularly due to its SWIR bands. Meanwhile, Sentinel-2A provided improved spatial delineation. Despite its high spectral resolution, Hyperion showed limited performance in separating spectrally similar units. Misclassification was primarily associated with lithologies that had similar mineralogical compositions and terrain-related effects. The results demonstrate the efficacy of MF in conjunction with multispectral data for lithological mapping and underscore the significance of selecting suitable data–method combinations in geologically complex regions.​