# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 5 Discussion  
In this paper, we propose a hyperspectral pansharpening method based on ratio transformation and residual networks. Compared with other pansharpening methods, the proposed approach demonstrates superior performance in practical applications. While some existing methods have achieved breakthroughs in specific aspects, they still have notable drawbacks. The GFPCA and SFIM methods exhibit deficiencies in preserving spatial details, failing to effectively restore fine structures in the image. The HySure and CNMF methods perform well in terms of spatial detail preservation but suffer from poor color fidelity, particularly in areas such as lakes, where color distortion is more pronounced. The fusion performance of the DRCNN method is relatively moderate, especially for critical features such as the coastline, where noticeable shape distortion appears in the pseudo-color representation. The DRSAN method performs relatively well in preserving spatial details, but its fused images have lower resolution compared to the proposed method, resulting in inferior detail restoration and spectral fidelity. Both visually and quantitatively, the proposed method outperforms the comparison methods, achieving superior spectral fidelity and spatial detail retention. Visually, the proposed method distinctly delineates object boundaries, restores finer details, and ensures higher color fidelity, particularly in complex regions such as lakes and mountains. In terms of quantitative evaluation metrics, the proposed method excels in spectral fidelity-related metrics such as the Q and CC values and surpasses other methods in spatial fidelity assessment, demonstrating its ability to enhance image resolution and spectral consistency while preserving image details. The key innovations of the proposed method are as follows:  
1. The method directly performs fusion on the original images instead of relying on degraded panchromatic and hyperspectral images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \(\alpha\) & **Q** & **ERGAS** & **SAM** & **CC** & **RMSE** \\ \hline EO-1 & 0.1 & 0.4749 & 7.0828 & 7.5784 & 0.8375 & 8.9346 \\  & 0.5 & 0.8895 & 5.2749 & 6.8905 & 0.9142 & 7.7804 \\  & 0.8 & 0.9051 & 5.2682 & 5.3816 & 0.9194 & 7.4982 \\  & 0.9 & 0.9090 & 5.2104 & 5.3142 & 0.9212 & 7.4123 \\  & **1.0** & **0.9110** & **5.1973** & **5.2649** & **0.9221** & **7.3834** \\  & 1.1 & 0.9102 & 5.2156 & 5.2973 & 0.9218 & 7.4078 \\  & 1.2 & 0.9093 & 5.2984 & 5.3871 & 0.9205 & 7.5210 \\  & 5.0 & 0.7949 & 6.7854 & 5.8923 & 0.8642 & 9.3585 \\ \hline Chikusei & 0.1 & 0.8234 & 6.7384 & 5.9649 & 0.8197 & 9.5538 \\  & 0.5 & 0.9011 & 5.7876 & 4.8346 & 0.8879 & 8.6835 \\  & 0.8 & 0.8988 & 4.3892 & 3.8921 & 0.9252 & 7.5899 \\  & 0.9 & 0.9019 & 4.2681 & 3.6825 & 0.9280 & 7.4203 \\  & **1.0** & **0.9028** & **4.1904** & **3.6381** & **0.9298** & **7.3850** \\  & 1.1 & 0.9005 & 4.2893 & 3.7394 & 0.9271 & 7.4651 \\  & 1.2 & 0.8977 & 4.4217 & 3.8810 & 0.9243 & 7.6082 \\  & 5.0 & 0.8532 & 6.9678 & 4.0896 & 0.8563 & 8.9463 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics of different fusion algorithms on EO-1 and Chikusei datasets. 2. The initial ratio image, generated through the ratio transformation method, is refined using a residual network to produce a new ratio image, which injects spatial details into the hyperspectral image. 3. Spatial-preserving and spectral-preserving loss functions are designed to constrain both spatial and spectral details of the fused image, enabling precise adjustment of the fusion process. However, the proposed method still has some limitations. First, when processing large-scale or high-resolution images, the computation time may be relatively long, potentially leading to performance bottlenecks in real-time applications. Second, the choice of parameters in the method can significantly influence the final fusion results in different application scenarios.