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Article

Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?

1
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China
2
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
3
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
*
Author to whom correspondence should be addressed.
Received: 7 February 2020 / Revised: 4 March 2020 / Accepted: 5 March 2020 / Published: 9 March 2020
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data. View Full-Text
Keywords: hyperspectral remote sensing; GF-5; GF-1; GF-2; S2A; data fusion hyperspectral remote sensing; GF-5; GF-1; GF-2; S2A; data fusion
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MDPI and ACS Style

Ren, K.; Sun, W.; Meng, X.; Yang, G.; Du, Q. Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used? Remote Sens. 2020, 12, 882. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050882

AMA Style

Ren K, Sun W, Meng X, Yang G, Du Q. Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used? Remote Sensing. 2020; 12(5):882. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050882

Chicago/Turabian Style

Ren, Kai, Weiwei Sun, Xiangchao Meng, Gang Yang, and Qian Du. 2020. "Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?" Remote Sensing 12, no. 5: 882. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050882

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