# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results
### Comparative Experiment  
To comprehensively evaluate the effectiveness of our method, we compared it with six representative pansharpening algorithms, including both traditional and deep learning-based techniques. These methods are guided filtering-based principal component analysis \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Q** & **ERGAS** & **SAM** & **CC** & **RMSE** & **Time (s)** \\ \hline Baseline & 0.8817 & 6.1072 & 5.4612 & 0.9122 & 7.9861 & **1.9335** \\ Baseline + Ratio & 0.8964 & 5.4385 & 5.3127 & 0.9159 & 7.6337 & 2.0416 \\ Baseline + RA & 0.9013 & 5.5098 & 5.3762 & 0.9184 & 7.5742 & 2.1069 \\ Proposed Method & **0.9104** & **5.1973** & **5.2649** & **0.9221** & **7.3834** & 2.1149 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation study of the proposed method on the EO-1 dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Q** & **ERGAS** & **SAM** & **CC** & **RMSE** & **Time (s)** \\ \hline Baseline & 0.8821 & 4.7278 & 3.8449 & 0.9182 & 7.9367 & **0.6031** \\ Baseline + Ratio & 0.8912 & 4.5017 & 3.7048 & 0.9235 & 7.7123 & 0.6248 \\ Baseline + RA & 0.8963 & 4.4332 & 3.6714 & 0.9256 & 7.5689 & 0.6441 \\
**Proposed Method** & **0.9028** & **4.1904** & **3.6381** & **0.9298** & **7.3851** & 0.6534 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study of the proposed method on the Chikusei dataset. fusion (GFPCA) [31], which applies PCA followed by guided filtering to enhance spatial resolution while maintaining spectral structure; HySure [8], which uses an unbiased risk estimation framework to perform spectral-spatial fusion; the smooth filtering-based intensity modulation method SFIM [32], which enhances edges through spatial filtering; CNMF [7], which decomposes hyperspectral and panchromatic images via matrix factorization for reconstruction; DRCNN [11], which integrates deep residual learning in a Laplacian pyramid structure; and DRSAN [25], which incorporates spatial attention into a deep residual network to preserve spatial details. While these methods have achieved progress in specific aspects--such as GFPCA and SFIM ensuring color fidelity, or HySure and CNMF improving edge sharpness--they often struggle to achieve a balanced preservation of both spatial and spectral features. Deep learning methods like DRCNN and DRSAN offer enhanced representation capability but still suffer from shape deformation or insufficient spectral consistency. In contrast, our proposed method explicitly injects spatial details via a ratio transformation mechanism and fine-tunes it with a residual attention network. The dual-loss design jointly optimizes spectral and spatial fidelity, offering a better trade-off and interpretability. During the training phase, the spatial resolution of the hyperspectral image samples was set to \(30\times 30\) pixels, and that of the panchromatic image samples was set to \(90\times 90\) pixels. The model was trained with a batch size of 16 and an initial learning rate of 0.001. A decay factor of 0.99 was applied every 10,000 steps. The RMSProp optimizer was employed to optimize the model. Throughout training, the learnable parameter set \(\theta\) of the residual attention network was iteratively updated via backpropagation based on the total loss function \(L=L_{p}+aL_{h}\). Initialized randomly, \(\theta\) was optimized to simultaneously reduce both spatial and spectral distortions. The empirical results demonstrate that the network reliably converges to a stable configuration under the specified learning rate and decay schedule. This optimization enables the model to effectively preserve fine spatial details while maintaining high spectral fidelity in the fused images. To ensure reproducibility, the experimental settings for the baseline methods CNMF and HySure are detailed as follows. For CNMF, the number of outer and inner loops was set to 30 and 5, respectively, with reconstruction error minimized using a least-squares criterion.