## 4 Results
### Virtual Image Generation Results
#### 4.1.2 Prediction Performance for Sentinel-2-Based Feature Combinations  
Table 9 summarizes the comparison results of the three CGAN models with different Sentinel-2 band combinations, as listed in Table 6. Based on the comparison results of the SAR-based feature combinations, three SAR-based features, including two backscattering coefficients and RVI, were used for the three CGAN models. When comparing the conventional CGAN models that utilized inputs with a single spatial resolution (CGAN\({}_{\text{VNIR}}\) and CGAN\({}_{\text{RESW}}\)), the prediction using CGAN\({}_{\text{2S}}\) showed superior predictive performance across all the spectral bands. In particular, the effect of a multi-band combination was much more significant for the prediction of the VNIR bands. The use of red-edge and SWIR bands as additional features could improve the RMSE from 37.5% to 54.61%. The maximum improvement in RMSE was achieved in relation to the NIR band. Although the resampled red-edge and SWIR bands were utilized as inputs,  
Figure 10: Comparison of prediction results for different SAR-based features with the actual Sentinel-2 imagery. The first and second rows represent the true and false color composite (NIR-red-green as RGB) images, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Band**} & \multicolumn{4}{c}{**CGAN\({}_{\text{VNIR}}\)**} & \multicolumn{4}{c}{**CGAN\({}_{\text{RESW}}\)**} & \multicolumn{4}{c}{**CGAN\({}_{\text{2S}}\)**} \\ \cline{2-13}  & **RMSE** & **CC** & **SSIM** & **PSNR** & **RMSE** & **CC** & **SSIM** & **PSNR** & **RMSE** & **CC** & **SSIM** & **PSNR** \\ \hline Blue & 0.032 & 0.600 & 0.853 & 30.027 & & & & 0.020 & 0.614 & 0.945 & 34.192 \\ Green & 0.036 & 0.649 & 0.865 & 28.874 & & & & 0.020 & 0.673 & 0.944 & 33.848 \\ Red & 0.050 & 0.691 & 0.745 & 26.087 & & & & 0.025 & 0.708 & 0.913 & 31.987 \\ NIR & 0.141 & 0.771 & 0.569 & 17.040 & & & & 0.064 & 0.799 & 0.720 & 23.846 \\ \hline RE1 & & & & & 0.027 & 0.687 & 0.904 & 31.379 & 0.022 & 0.815 & 0.931 & 33.197 \\ RE2 & & & & & 0.055 & 0.662 & 0.767 & 25.167 & 0.043 & 0.820 & 0.828 & 27.435 \\ RE3 & & & & & 0.076 & 0.675 & 0.664 & 22.351 & 0.057 & 0.833 & 0.756 & 24.816 \\ SWIR2 & & & & & 0.036 & 0.751 & 0.882 & 28.782 & 0.027 & 0.864 & 0.921 & 31.376 \\ SWIR3 & & & & & 0.028 & 0.730 & 0.900 & 30.928 & 0.023 & 0.832 & 0.932 & 32.856 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy statistics of three CGAN models with different Sentinel-2 feature combinations (RE: red-edge; NIR: near infrared; SWIR: shortwave infrared; RMSE: root mean square error; CC: correlation coefficient; SSIM: structural similarity; PSNR: peak signal-to-noise ratio). the features from the highly correlated red-edge2 and red-edge3 bands could improve the prediction accuracy of the NIR band. The SSIM values for the red and NIR bands were also improved by 16.8%p and 15.1%p, respectively. The accuracy improvement was relatively minor when comparing the predictive performance of CGAN2S with CGANRESW. However, the accuracy values of the red-edge and SWIR bands predicted by CGAN2S were still much greater than that of CGANRESW. The use of the VNIR bands with finer spatial resolutions could improve the predictive accuracy of the red-edge and SWIR bands. These comparison results demonstrate the necessity of a multi-band combination to predict optical imagery. The visual comparison results in two subareas are shown in Figure 11. The NIR and red-edge3 bands were selected for the visual comparison because their sensitivity to vegetation vitality is helpful for crop classification. For the NIR band prediction, CGANVIR and CGAN2S generated similar results in this subarea, although their RMSE values over the study area were significantly different. In contrast, CGANRESWR generated much-degraded prediction results for the red-edge3 band. In particular, spectral distortion in the prediction results was severe in the left subarea consisting of lakes, developed areas, and grassland.