## 3 Results and Analysis  
Fusion experiments were performed based on the GF-1 and GF-5 images, which were acquired on 28 May 2019 and 29 May 2019, respectively. The types of land cover are mainly rivers, vegetation, soil, and buildings. Figure 3 shows the image data after preprocessing, and Figure 4 shows the fused images after applying the PCA, GS transform, HA, and HGF methods. It can be seen from the figure that the visual effect of the fused image is better than that of the original image, with clearer textures and improved spatial resolution. Figure 3: Preprocessed image data: (**a**) GF-1 Band 4 (**b**) GF-5 image. Furthermore, CC, STD, PSNR, and SAM were used as metrics to quantitatively evaluate the spatial information incorporation and spectral information fidelity of the different fusion methods, as shown in Figure 5 and Table 2. Figure 5 shows the spectral curves of the pure image elements at the same position in different fusion images. By comparing the original and fused image obtained after applying different processing methods, we can observe from the CC index that the optimal methods in the 390-730 nm, 730-1400 nm, and 1400-2260 nm intervals are GS, HA, and HGF, respectively. A larger STD reflects richer spatial information in the image; thus, the optimal method in all three intervals is HGF. PSNR can reflect whether the image is distorted; the higher the value, the better the image fusion quality. Therefore, the best method in the three intervals is HGF. The smaller the SAM, the smaller the angle between the two spectral curves, and the better the fusion effect. In the 390-730 nm, the GS method is best suited for studying soils, buildings, vegetation, and water. The best fusion method for the four features is HGF in the 730-1400 nm interval. In the 1400-2260 nm interval, the best fusion method for soil studies is GS, while HGF is more suitable for buildings, vegetation, and water. To further analyze the applicability and accuracy of the fusion methods for different data sources, we used ZY1-02D and Sentinel-2B images. The fused images obtained using the PCA, GS, HA, and HGF algorithms are shown in Figure 6. Both qualitative and quantitative analyses were performed, and it was confirmed that the HGF method had the best per \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Wavelength Range**} & \multicolumn{4}{c}{**390-730 nm**} & \multicolumn{4}{c}{**730-1400 nm**} & \multicolumn{4}{c}{**1400–2260 nm**} \\ \hline \multicolumn{2}{c}{**Method**} & **PCA** & **GS** & **HA** & **HGF** & **PCA** & **GS** & **HA** & **HGF** & **PCA** & **GS** & **HA** & **HGF** \\ \hline \multirow{8}{*}{Evaluation metrics} & CC & \(-0.14\) & **0.86** & 0.74 & 0.79 & 0.08 & 0.76 & **0.93** & 0.82 & \(-0.62\) & 0.71 & 0.82 & **0.85** \\ \cline{2-11}  & STD & 0.90 & 3.64 & 2.82 & **4.22** & 1.10 & 3.75 & 2.92 & **3.91** & 3.48 & 4.29 & 2.65 & **4.59** \\ \cline{2-11}  & PSNR & 13.92 & **19.80** & 13.68 & 18.02 & 8.35 & 15.63 & 13.75 & **16.78** & 9.14 & 15.69 & 13.62 & **17.62** \\ \cline{2-11}  & Soil & 0.39 & **0.02** & 0.20 & 0.10 & 0.16 & 0.01 & 0.02 & **0.01** & 0.17 & **0.01** & 0.03 & 0.02 \\ \cline{2-11}  & Building & 0.26 & **0.01** & 0.47 & 0.04 & 0.09 & 0.01 & 0.04 & **0.01** & 0.16 & 0.01 & 0.05 & **0.01** \\ \cline{2-11}  & Vegetation & 0.03 & **0.03** & 0.49 & 0.07 & 0.01 & 0.01 & 0.02 & **0.01** & 0.01 & 0.02 & 0.11 & **0.01** \\ \cline{2-11}  & Water bodies & 0.32 & **0.31** & 0.31 & 0.35 & 0.79 & 0.84 & 0.75 & **0.74** & 0.95 & 0.98 & 0.89 & **0.87** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fusion accuracies. Figure 4: Fused images obtained using **(a)** PCA, **(b)** GS transform, **(c)** HA, and **(d)** HGF processing. Figure 5: Land cover spectral information using different fusion methods. formance (see Figure 7 and Table 3). It provided a filtering effect and improved image quality. Figure 7 shows the spectral curves of the pure image elements at the same position in images obtained using different fusion methods.