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

Kai Ren,Xiangchao Meng,Qian Du,Gang Yang,Weiwei Sun

doi:10.3390/rs12050882

Abstract

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.

Highlights

Hyperspectral imaging sensors generally collect more than 100 spectral bands with a wavelength range within 400–2500 nm
The China GaoFen-5 (GF-5) satellite was launched on 9 May 2018, and one of its six main payloads is an advanced hyperspectral (HS) imager developed by the Shanghai Institute of Technical Physics (SITP), Chinese Academy of Sciences
The reason for this is that GSA injects spatial details of MS data into HS data via component substitution and has an ideal transformation coefficient to maintain the spectral separation of HS data

Summary

Introduction

Hyperspectral imaging sensors generally collect more than 100 spectral bands with a wavelength range within 400–2500 nm. The GF-5 HS imager has 330 spectral bands ranging from 400 to 2500 nm, with a spectral resolution of 5 nm in VNIR (visible/near-infrared) and 10 nm in SWIR (short-wave infrared), respectively. It acquires HS images with a spatial resolution of 30 m and a swath width of 60 km. Its spatial resolution surpasses or equals those of most on-orbit or planned spaceborne HS imagers; e.g., DESIS, HysIS, PRISMA, HISUI and EnMAP. The GF-5 HS data are intended to serve China’s natural resource surveying and monitoring; e.g., mineral exploration, water body monitoring and vegetation mapping, and ecological environment protection—e.g., soil heavy metal pollutant mapping and ecological disaster prevention and mitigation [6,7,8,9]

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Methods

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Discussion

Conclusion