## 3 Results  
Two experiments were conducted to evaluate our results: (1) whether to use 60 m images and (2) the effects of the feature dimension sizes. The EDSR neural network was also trained for comparison with our results. EDSR was selected as a comparison due to its excellent performance among the currently developed SR neural networks [12]. [PERSON] et al. [11] designed both baseline and EDSR models. The difference between the two models is the number of residual blocks and the feature dimension. The baseline model is organized with 16 residual blocks and 64 feature dimensions, and EDSR is formed with 32 residual blocks and 256 feature dimensions. Both models are utilized for comparison, and all related training parameters were set as the author suggested. After training both neural networks with the same datasets, the results were evaluated with three metrics. PSNR and SSIM were used to evaluate the outcome--they are most frequently used as an evaluation index of SR deep learning research [11; 12; 20]. The RMSE used in some studies [6; 9] was also included. The comparison of the final three metrics is summarized in Table 3 for Daejeon City and Table 4 for Sejong City, respectively. The scale parameters 2 and 4 refer to generating 5.0 m and 2.5 m aerial orthoimages, respectively. In the case of Daejeon City, our context-based ResU-Net outperformed the baseline and EDSR models for all three metrics. For EDSR, even if the residual blocks and feature dimensions increased comparing with the baseline model, it is difficult to find performance improvement. In the case of Sejong City, in which independent testing was performed, our models performed better in two metrics except for RMSE. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Neural Networks**} & \multirow{2}{*}{**Use of 60 m**} & \multicolumn{2}{c}{**Feature Dimensions**} & \multirow{2}{*}{**Epoch**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} \\ \cline{3-3} \cline{6-7}  & Baseline & Y & & & 120 & 22.9871 & 22.2210 & 0.4750 \\ \cline{3-7}  & (EDSR) & N & & & 100 & 23.1250 & 22.1883 & 0.4738 \\ \cline{2-7}  & \multirow{2}{*}{EDSR} & Y & & & 70 & 22.5078 & 22.3772 & 0.4834 \\ \cline{3-7}  & & N & & 100 & 21.9486 & 22.6070 & 0.4935 \\ \cline{3-7}  & \multirow{2}{*}{\multirow{2}{*}{\begin{tabular}{c} Context-based \\ ResU-Net (Ours) \\ \end{tabular} }} & \multirow{2}{*}{Y} & \(f_{a}\) & & 20.2371 & 23.3116 & 0.5010 \\ \cline{3-7}  & & Y & \(f_{b}\) & & 19.3775 & 23.6701 & 0.5233 \\ \cline{3-7}  & & & \(f_{c}\) & & 18.6816 & 23.9578 & 0.5437 \\ \cline{3-7}  & \multirow{2}{*}{\begin{tabular}{c} Context-based \\ ResU-Net (Ours) \\ \end{tabular} } & \multirow{2}{*}{N} & \(f_{a}\) & & 20.2274 & 23.3333 & 0.5005 \\ \cline{3-7}  & & & \(f_{b}\) & & 19.4900 & 23.6332 & 0.5234 \\ \cline{3-7}  & & & \(f_{c}\) & & **18.8066** & **23.8895** & 0.5439 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Baseline (EDSR) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Baseline \\ N \\ \end{tabular} } & Y & & 64 & 180 & 24.6372 & 21.5330 & 0.3675 \\ \cline{3-7}  & & N & & 24.7514 & 21.5305 & 0.3648 \\ \cline{3-7}  & \multirow{2}{*}{\begin{tabular}{c} EDSR \\ N \\ \end{tabular} } & Y & & 30 & 26.4536 & 20.9607 & 0.3516 \\ \cline{3-7}  & & N & & 40 & 26.7308 & 20.9715 & 0.3572 \\ \cline{3-7}  & & & \(f_{a}\) & & 22.9141 & 22.1295 & 0.3770 \\ \cline{3-7}  & \multirow{2}{*}{\
\begin{tabular}{c} Context-based \\ ResU-Net (Ours) \\ \end{tabular} } & Y & & \(f_{b}\) & & 22.1827 & 22.3758 & 0.3888 \\ \cline{3-7}  & & & \(f_{c}\) & & 21.3574 & 22.6966 & 0.4101 \\ \cline{3-7}  & \multirow{2}{*}{
\begin{tabular}{c} N \\ \end{tabular} } & \multirow{2}{*}{N} & \(f_{a}\) & & 22.9897 & 22.1006 & 0.3774 \\ \cline{3-7}  & & & \(f_{b}\) & & 22.1444 & 22.3971 & 0.3912 \\ \cline{3-7}  & & & \(f_{c}\) & & **21.7778** & **22.5278** & **0.4018** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quality evaluation results with set parameters for Daejeon City.