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    LI Zhizhong,CHEN Shengbo,LIU Dechang,et al. Progress of Remote Sensing Geological Prospecting Domestic and Abroad in Recent Years[J]. Northwestern Geology,2025,58(3):183−195. doi: 10.12401/j.nwg.2025069
    Citation: LI Zhizhong,CHEN Shengbo,LIU Dechang,et al. Progress of Remote Sensing Geological Prospecting Domestic and Abroad in Recent Years[J]. Northwestern Geology,2025,58(3):183−195. doi: 10.12401/j.nwg.2025069

    Progress of Remote Sensing Geological Prospecting Domestic and Abroad in Recent Years

    More Information
    • Received Date: March 26, 2025
    • Revised Date: May 05, 2025
    • Accepted Date: May 06, 2025
    • Available Online: May 13, 2025
    • In recent years, with the rapid development of satellite, aerial and ground remote sensing data sources and processing technology, significant progress has been made in the technical methods and applications of remote sensing geological prospecting at home and abroad. In this paper, major satellite remote sensing data, aerial hyperspectral remote sensing data and ground data are systematically reviewed. Among them, multi-spectral images such as Landsat-8, ASTER and Sentinel-2 are the most widely used, and hyperspectral remote sensing data of domestic satellites such as GF-5 and ZY1-02D have covered most of the world's land area. It can meet the demand of global mineral resources exploration data and show great application potential and social and economic benefits. UAV hyperspectral sensors such as Headwall, HySpex and SSMAP have great potential in lithology and mineral identification at mining area scale. Remote sensing technology has achieved good application results in lithology classification, mineralization alteration information extraction, structure extraction and remote sensing prospecting model. With the development of artificial intelligence technology, it will play a greater role in remote sensing geological prospecting. At present, remote sensing geological prospecting still faces problems such as weak ore information extraction and scale difference of remote sensing data in complex landscape areas such as vegetated areas. In the future, further exploration is needed in multi-source remote sensing data fusion technology, broader application expansion and artificial intelligence prospecting application.

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