{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:38:16Z","timestamp":1770755896302,"version":"3.50.0"},"reference-count":58,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Science and Technology Project of the Xinjiang Science Foundation for Distinguished Young Scholars","award":["2022D01E01"],"award-info":[{"award-number":["2022D01E01"]}]},{"name":"Major Science and Technology Project of the Xinjiang Science Foundation for Distinguished Young Scholars","award":["2022TSYCLJ0010"],"award-info":[{"award-number":["2022TSYCLJ0010"]}]},{"name":"Major Science and Technology Project of the Xinjiang Science Foundation for Distinguished Young Scholars","award":["2023B03019-3"],"award-info":[{"award-number":["2023B03019-3"]}]},{"name":"Major Science and Technology Project of the Xinjiang Science Foundation for Distinguished Young Scholars","award":["2023B03006-4"],"award-info":[{"award-number":["2023B03006-4"]}]},{"name":"\u201cTianshan Talents\u201d training program","award":["2022D01E01"],"award-info":[{"award-number":["2022D01E01"]}]},{"name":"\u201cTianshan Talents\u201d training program","award":["2022TSYCLJ0010"],"award-info":[{"award-number":["2022TSYCLJ0010"]}]},{"name":"\u201cTianshan Talents\u201d training program","award":["2023B03019-3"],"award-info":[{"award-number":["2023B03019-3"]}]},{"name":"\u201cTianshan Talents\u201d training program","award":["2023B03006-4"],"award-info":[{"award-number":["2023B03006-4"]}]},{"name":"Xinjiang Uygur Autonomous Region key research and development program","award":["2022D01E01"],"award-info":[{"award-number":["2022D01E01"]}]},{"name":"Xinjiang Uygur Autonomous Region key research and development program","award":["2022TSYCLJ0010"],"award-info":[{"award-number":["2022TSYCLJ0010"]}]},{"name":"Xinjiang Uygur Autonomous Region key research and development program","award":["2023B03019-3"],"award-info":[{"award-number":["2023B03019-3"]}]},{"name":"Xinjiang Uygur Autonomous Region key research and development program","award":["2023B03006-4"],"award-info":[{"award-number":["2023B03006-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study investigates the application of hyperspectral image space\u2013spectral fusion technology in lithologic classification, using data from China\u2019s GF-5 and Europe\u2019s Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space\u2013spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The \u201cGSA+RF\u201d method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.<\/jats:p>","DOI":"10.3390\/s24041267","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T03:18:38Z","timestamp":1708312718000},"page":"1267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3329-3036","authenticated-orcid":false,"given":"Yujin","family":"Chi","sequence":"first","affiliation":[{"name":"Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"},{"name":"Xinjiang Research Center for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9459-9074","authenticated-orcid":false,"given":"Nannan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"},{"name":"Xinjiang Research Center for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Liuyuan","family":"Jin","sequence":"additional","affiliation":[{"name":"Geological Survey Academy of Xinjiang, Urumqi 830000, China"}]},{"given":"Shibin","family":"Liao","sequence":"additional","affiliation":[{"name":"Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"},{"name":"Xinjiang Research Center for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"},{"name":"Xinjiang Research Center for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[{"name":"Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China"},{"name":"Xinjiang Research Center for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cao, M., Bao, W., and Qu, K. 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