Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data and Random Forest Algorithm

Airborne hyperspectral remote sensing data provide rapid, non-destructive, and near laboratory quality reflectance spectra for mineral mapping lithological discrimination, thereby ushering an innovative era of sensing. In this study, NEO HySpex cameras, which comprise 504 spectral channels in the ranges 0.4–1.0 ?m 1.0–2.5 ?m, were mounted on a delta wing XT-912 aircraft. The designed flexibility modular nature aircraft imaging system made it relatively easy to test, transport, install, remove multiple times before acquisition flights. According design fight plan, including route distance, length, height, flight speed, we acquired high spatial resolutions airborne images Yudai porphyry Cu (Au, Mo) mineralization Kalatag District, Eastern Tianshan terrane, Northwest China. By comparing features standard from United States Geological Survey database, endmember pixels signatures most alteration assemblages (goethite, hematite, jarosite, kaolinite, calcite, epidote, chlorite) extracted. After processing workflow, distribution (iron oxide/hydroxide, clay, propylitic alterations) was mapped using random forest (RF) algorithm. experiments demonstrated that workflow RF algorithm is feasible active, show good performance classification accuracy. overall accuracy Kappa identification 73.08 65.73%, respectively. main primarily distributed around pits grooves, consistent with field-measured data. Our results confirm have potential application basic geology survey exploration, viable alternative identifying units at resolution large areas inaccessible terrains.