# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results
### Comparative Experiment
#### 4.5.2 Chikusei Dataset  
The pseudo-color fusion results of different fusion methods on the Chikusei dataset are shown in Figure 6. The pseudo-color composite bands are as follows: Red 60, Green 40, and Blue 20. From the figure, it can be seen that methods such as GFPCA, SFIM, and DRCNN exhibit noticeable deviations in both spatial details and color fidelity. However, HySure, CNMF, DRSAN, and the method proposed in this paper show better fusion results in terms of both spatial details and color. Among these, the method proposed in this paper preserves the spatial details most clearly.  
Table 4 presents the evaluation metrics of different fusion algorithms on the Chikusei dataset. From the table, it can be seen that the proposed method in this chapter achieves the best results in terms of the Q value and CC value. Additionally, it exhibits the smallest values in ERGAS, SAM, and RMSE, indicating the best performance. Although the running time of the proposed method is not the shortest, it is considered to be at an above-average level compared to other fusion methods. The slightly longer runtime is primarily attributed to the use of two residual spatial attention modules, which enhance feature represen  
Figure 5: Comparison of spectral reflectance differences at four randomly selected locations on the EO-1 dataset: (a) (129, 226), (b) (138, 72), (c) (89, 90), and (d) (158, 76). Each curve represents the reflectance difference between a specific fusion method and the reference hyperspectral image at each spectral band. The closer the curve is to 0 (the black dashed line), the higher the spectral fidelity. Note that all negative values in the plot are indicated using minus signs (e.g., \(-1\) instead of hyphens).  
tation through multiple convolutional operations and dual-scale attention mechanisms (MSA and USA). These modules effectively preserve fine spatial details but introduce additional computational overhead during both training and inference. Furthermore, the ratio transformation stage involves image degradation and refinement processes, which also contribute to the increase in computational complexity.  
Figure 7 shows the comparison of spectral reflectance differences between different fusion methods and the baseline (black dashed line) at four randomly selected coordinates  
Figure 6: Fusion performance of different methods on the Chikusei dataset. It is recommended to conduct a comparative analysis from both spatial and spectral perspectives: on the one hand, focus on the clarity of object edges and texture details in the image; on the other hand, evaluate the naturalness and accuracy of color reproduction.  
on the Chikusei dataset. The coordinates of the randomly selected points are as follows: (a) (42,189), (b) (58,93), (c) (90,147), and (d) (20,149). From the figure, it is visually evident that at the four randomly selected coordinates, the proposed method has the smallest spectral deviation compared to the baseline. HySure shows good spectral preservation at (a), (b), and (d), but exhibits significant fluctuations in some bands at (c). The spectral deviation of the fused image using the SFIM method is the largest.