Mafic-ultramafic intrusive complexes possess a considerable capacity for hosting Ni-Cu-platinum group element (PGE) sulfide deposits. However, the mapping of small outcrops over large areas by field surveys is time-consuming. In this study, WorldView-3 (WV-3) data with moderate spectral and very high spatial resolution were employed for mapping mafic-ultramafic units using spectral indices and the spatial-spectral transformer (SSTF) deep learning method in the Luotuoshan area of Beishan, Gansu Province, China. Based on representative reflectance signatures extracted from imagery of known locations, false-color composites of three-band ratios and a newly proposed shortwave infrared (SWIR) spectral index provided reasonable delineation of mafic-ultramafic rocks. The SSTF method facilitated mapping the occurrence of small mafic-ultramafic outcrops and defining much clearer boundaries, particularly for tiny units at meter scale. Moreover, the SSTF method is not sensitive to the occurrence of carbonate lenses that may affect the reflectance of outcrops. Field investigation and laboratory sample analyses confirmed the occurrence of mafic and ultramafic rocks with substantial metallic mineral potential in this area. Seven prospects were confirmed to be related to mafic-ultramafic intrusions during field validation, four of which contained metallic minerals such as chalcopyrite, pentlandite, pyrite, and chromite in the samples observed by scanning electron microscopy and energy dispersive spectrometry. This study proves that the spectral indices and SSTF deep learning method applied on WV-3 multispectral data are useful for discriminating small-sized mafic-ultramafic intrusive rocks (<100 m) for prospecting of local mineralization.

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