Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study

Authors:
raw_author_name: Yujin Chi
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
raw_author_name: Nannan Zhang
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
raw_author_name: Shibin Liao
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
raw_author_name: Hao Zhang
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China
raw_author_name: Li Chen
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi 830011, China

Abstract: This study investigates the application of hyperspectral image space-spectral fusion technology in lithologic classification, using data from China's GF-5 and Europe's Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space-spectral 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 "GSA+RF" 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.