Comparison of fusion methods on GF-5 hyperspectral data

ZHANG, Lifu; Zhao, X.; Sun, Xia; Huang, Hai Bin; Peng, M; CEN, Yi; Tu, Keling

doi:10.11834/jrs.20229318

Cited by 3 publications

(1 citation statement)

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“…Since the 2018 launch of GF-5, the adaptability of fusion algorithms has drawn widespread attention in the past two years. The highest spatial resolution of Sentinel-2A data is 10 m, offering a fusion ratio greater than 1:4 with GF-5 [ 45 , 46 ]. Therefore, this paper selects GF-5 and Sentinel-2A to explore the adaptability of fusion algorithms, choosing one representative method from each of the five categories for experimentation.…”

Section: Introductionmentioning

confidence: 99%

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

Chi,

Zhang,

Jin

et al. 2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%