数据融合是解决高光谱卫星在时空分辨率等指标上受限的有效途径，探讨不同方法在GF-5高光谱数据上的融合效果，对GF-5高光谱数据的信息挖掘与推广应用有着重要意义。本文本着算法简单易用、适于推广的原则，采用GS（Gram-Schmidt）葛兰—施密特正交变换融合算法、GSA（GS Adaptive）自适应GS融合算法、CNMF（Coupled Non-negative Matrix Factorization）耦合非负矩阵分解融合算法、CRISP-W（Color Resolution Improvement Software Package with Wavelet transform）基于小波变换和CRISP-B（Color Resolution Improvement Software Package with Butterworth）基于巴特沃斯滤波器的分辨率提升融合算法、GLP（Generalized Laplacian Pyramid）广义拉普拉斯金字塔融合算法共6种融合方法，分别对BJ-2、GF-2、GF-1、GF-1C、GF-1D国产卫星多光谱数据与GF-5高光谱数据进行融合实验。通过目视分析、指标评价（相关系数、通用图像质量指标、峰值信噪比、光谱角、全局综合误差）、分类应用、时间成本4种方式对融合结果进行综合比较分析。结果表明，相融合的一组图像系列相同、空间分辨率相差越小，融合结果越好。CRISP-B、CRISP-W、GLP在提升空间分辨率、光谱保真度方面能达到较好的平衡，空间重建方面，GLP稍优且更稳定，CRISP-B、CRISP-W则在光谱信息保持方面稳定性更强且效果更好。数据源会对融合方法产生一定的影响，在光谱特征信息提取、分析等对光谱保真度要求高的工作中，GLP更适合同源数据（如GF-5与GF-1/1C/1D/2）融合，而在多源数据间（如GF-5与BJ-2）进行融合时，则优先选择CRISP-W。CNMF存在一定程度的色彩畸变，且运行时间较长。GSA、GS融合效果最差，其中，GSA不论是光谱保持能力还是空间分辨率提升能力均较GS更稳定。在小样本高光谱图像分类应用中，CRISP-B融合结果分类效果稳定，分类精度较高。GSA融合结果空间细节丰富，虽光谱失真较为严重，但同时增大了地物光谱分离度，仍适用于准确勾勒建筑物、道路等地物。本研究为GF-5高光谱数据与其他国产卫星多光谱数据融合方法的选择提供参考，有助于高分五号高光谱数据的应用与推广。

Data fusion is an effective way to solve the limitation of hyperspectral satellites on temporal and spatial resolution. Discussing the fusion effects of different methods on GF-5 hyperspectral data is highly important for information mining and promotion application of GF-5 hyperspectral data.In this study, based on the principle that the algorithm is easy to use and suitable for generalization, six fusion methods, namely, GS (Gram-Schmidt), GSA (GS Adaptive), CNMF (Coupled Non-negative Matrix Factorization), CRISP-B, CRISP-W (Color Resolution Improvement Software Package with Butterworth or Wavelet transform), GLP (Generalized Laplacian Pyramid) are separately used to perform fusion experiments on GF-5 hyperspectral data and multispectral data from BJ-2, GF-2, and GF-1/1C/1D domestic satellites. Visual interpretation, five indicators (correlation coefficient, universal image quality index, spectral angle mapper, erreur relative globale adimensionnelle de synthèse, and peak signal-to-noise ratio), classification application, and time costs are used to comprehensively evaluate the fusion results.Results show that the fusion image series are the same and the smaller the spatial resolution difference, the better the fusion result. CRISP-B, CRISP-W, and GLP can achieve a good balance in improving spatial resolution and spectral fidelity. In terms of spatial reconstruction, GLP is slightly better and more stable, while CRISP-B and CRISP-W are more stable and effective in maintaining spectral information. The data source will have a certain effect on the fusion method. In the tasks that require high spectral fidelity, such as spectral feature information extraction and analysis, GLP is more suitable for the fusion of homologous data (such as GF-5 and GF-1/1C/1D/2). When the multi-source images (GF-5 and BJ-2) are merged, CRISP-W is preferred. CNMF has a certain degree of color distortion and takes a long time to run. GSA and GS have the worst fusion effect. The spectral retention and the spatial resolution improvement ability of GSA are more stable than those of GS. Based on a small sample, the classification effect of the CRISP-B fusion result is stable and highly accurate. The GSA fusion results are rich in spatial details. Although the spectral distortion is relatively serious, it also increases the spectral distinction of the ground objects, which is still suitable for accurately drawing buildings and roads.This study provides method decision support for the fusion of GF-5 hyperspectral data and other domestic satellite multispectral data, which is helpful for the application and promotion of GF-5 hyperspectral data.