Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data and Random Forest Algorithm - Wang, Shanshan; Zhou, Kefa; Wang, Jinlin; Zhao, Jie (Frontiers in Earth Science, 2022)
Airborne hyperspectral remote sensing data provide rapid, non-destructive, and near laboratory quality reflectance spectra for mineral mapping and lithological discrimination, thereby ushering an innovative era of remote sensing. In this study, NEO HySpex cameras, which comprise 502 spectral channels in spectral ranges of 0.4–1.0 μm and 1.0–2.5 μm, were mounted on a delta wing XT-912 aircraft. According to the design fight plan, including the route distance, length, height, and flight speed, we acquired high-quality airborne hyperspectral images of the surface of Yudai porphyry Cu (Au, Mo) mineralization in the Kalatag district, Eastern Tianshan terrane, Northwest China. Comparing the features of the HySpex hyperspectral data and standard spectra data from the United States Geological Survey database, endmember pixels of spectral signatures for most alteration mineral assemblages (goethite, hematite, jarosite, kaolinite, calcite, epidote, and chlorite) were extracted. After data preprocessing and method workflow, the distribution of alteration mineral assemblages (iron oxide/hydroxide, clay, and propylitic alterations) was mapped using the random forest algorithm. The main alteration mineral assemblages were primarily distributed around pits and grooves, which is consistent with field-measured data. The overall classification accuracy and kappa classification of alteration mineral identification were 73.08% and 65.73%, respectively, which meet the demand for land use monitoring. Our results confirm that HySpex airborne hyperspectral data have potential application in basic geology survey and mineral exploration, which provide a viable alternative for mineral mapping and the identification of lithological units at high spatial resolution for large areas and inaccessible terrain.