## 3 Results
Blue indicates the best results, and red indicates the second best. The image quality deteriorated as the magnification was enlarged, and the value of the evaluation metrics gradually deteriorated. Through fine-tuning, the inclusion of 60 m images performs better in two networks. When 60 m images were included, RMSE decreased, and PSNR and SSIM increased. This result demonstrates that the 60 m images have a positive impact on both networks. The loss converged to a lower value if feature dimensions increased, as depicted in Figure 7. The result also validated that the denser the neural network, the higher the quality. Moreover, we found the accuracy is much higher when both denser feature dimensions and the 60 m images were used. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scale** & **Neural Networks** & **Use of 60 m** &
\begin{tabular}{c} **Feature** \\ **Dimensions** \\ \end{tabular} & **RMSE** & **PSNR** & **SSIM** \\ \hline \multirow{6}{*}{2} & Baseline & Y & \multirow{2}{*}{64} & 30.3523 & 19.3793 & 0.4034 \\ \cline{3-6}  & (EDSR) & N & & 30.5805 & 19.3052 & 0.4009 \\ \cline{3-6}  & & Y & & 30.3873 & 19.4050 & 0.4086 \\ \cline{3-6}  & EDSR & N & & 30.0645 & 19.4856 & 0.4110 \\ \cline{3-6}  & & & \(f_{a}\) & 30.4532 & 19.4819 & 0.4125 \\ \cline{3-6}  & & Y & \(f_{b}\) & 30.8639 & 19.3829 & 0.4122 \\ \cline{3-6}  & Context-based & & & \(f_{c}\) & 30.4220 & 19.5121 & **0.4182** \\ \cline{3-6}  & \multirow{3}{*}{N} & & \(f_{a}\) & 30.5115 & 19.4902 & 0.4183 \\ \cline{3-6}  & & & \(f_{b}\) & **30.3297** & **19.5689** & 0.4173 \\ \cline{3-6}  & & & \(f_{c}\) & 30.4948 & **19.5190** & 0.4151 \\ \hline \multirow{6}{*}{4} & Baseline & Y & \multirow{2}{*}{64} & 31.5568 & 18.9689 & 0.3287 \\ \cline{3-6}  & (EDSR) & N & & 31.4554 & 18.9719 & 0.3270 \\ \cline{3-6}  & & Y & & 32.4436 & 18.7164 & 0.3188 \\ \cline{3-6}  & & N & & 33.1779 & 18.5482 & 0.3250 \\ \cline{3-6}  & & & \(f_{a}\) & 31.3203 & 19.0943 & 0.3357 \\ \cline{3-6}  & & Y & \(f_{b}\) & 31.5619 & 19.0178 & 0.3357 \\ \cline{3-6}  & Context-based & & & \(f_{c}\) & 31.6259 & 19.0225 & **0.3387** \\ \cline{3-6}  & \multirow{3}{*}{N} & & \(f_{a}\) & 31.8976 & 18.9556 & 0.3368 \\ \cline{3-6}  & & & \(f_{b}\) & 31.4533 & 19.1072 & 0.3382 \\ \cline{3-6}  & & & \(f_{c}\) & **31.3686** & **19.0990** & **0.3407** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quality evaluation results for Sejong City. Blue indicates the best results, and red indicates the second best. For a visual comparison between EDSR and context-based ResU-Net, the prediction images are listed in Tables 5-9. Each table shows the predicted image of one representative input Sentinel-2 image per resolution (10 m, 20 m, and 60 m) for two scales, 2 and 4. The 2.5 m and 5.0 m aerial orthoimages are GT. The use of the 60 m Sentinel-2 images is shown in the second column. The predicted images of the baseline and EDSR model are shown in the third column. There is not much difference between the baseline and EDSR model, and only EDSR will be compared in the following. The predicted images of our context-based ResU-Net for three feature dimensions (\(f_{a},f_{b},f_{c}\)) are shown in the fourth column of each table. Generally, the prediction images between EDSR and context-based ResU-Net are visually similar when the feature dimension of context-based ResU-Net is \(f_{a}\). For EDSR, even if the residual blocks and feature dimensions increase, no further improvement can be found. However, in our model, as networks become denser from \(f_{a}\) to \(f_{c}\), it can be seen that the prediction images are getting close to GT. Figure 7: Convergence results according to each feature dimension. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Use of 60 m**} & \multicolumn{2}{c}{**Predicted Images**} & \multirow{2}{*}{**Input Images per Each Resolution (Sentinel-2)**} \\ \cline{3-3} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Predicted images of Sentinel-2 and corresponding GT image (paddy/road area).