## 3 Experiment and Discussion
### Comparative Experiments
Our method's higher SSIM indicates better preservation of the image structure, textures, and edge details, making the generated images more similar to the original images in terms of structure. Finally, DSpix2 pix also outperforms pix2 pix on the LPIPS metric, which is used to evaluate perceptual image quality. LPIPS uses a pre-trained deep neural network to capture high-level features of images and assesses perceptual differences by calculating the differences between images in this feature space. A smaller LPIPS value indicates that the generated images appear more natural to the human eye, with richer details and fewer artifacts, suggesting better perceptual quality and a more natural visual effect. This indicates that the super-resolution results generated by DSpix2 pix not only have the highest similarity to the ground truth high-resolution images in terms of image structure but are also the least affected by noise. The visual quality of the generated images could potentially enhance the classification of remote sensing image. However, the network's performance on the ERGAS and SAM metrics is not ideal, indicating that although the generated images have improved visual quality, they may lack in global consistency and spectral consistency. This means that the high-resolution images reconstructed by DSpix2 pix may not be as suitable for fine-scale remote sensing parameter inversion tasks, such as soil moisture or biomass inversion. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Downsamples 4\(\times\)**} \\ \cline{2-7}  & **RMSE**\(\downarrow\) & **PSNR**\(\uparrow\) & **SSMI**\(\uparrow\) & **ERGAS**\(\downarrow\) & **LPIPS**\(\downarrow\) & **SAM**\(\downarrow\) \\ \hline Bicubic & 70.04 & 21.01 & 0.51 & **3.15** & 0.77 & 14.75 \\ pix2 pix & 78.16 & 19.87 & 0.52 & 4.70 & 0.42 & 15.20 \\ CycleGAN & 167.20 & 13.24 & 0.24 & 3.29 & 0.54 & 14.66 \\ DRIT & 179.19 & 12.84 & 0.28 & 3.30 & 0.66 & **14.38** \\ UNIT & 79.47 & 19.80 & 0.47 & 3.26 & 0.35 & 15.03 \\ SRGAN & 61.51 & 22.69 & 0.62 & 4.61 & 0.56 & 15.43 \\ DSpix2 pix & **48.35** & **24.23** & **0.72** & 3.35 & **0.34** & 15.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of generation effects: DSpix2 pix vs. other networks in 4\(\times\) super-resolution tasks. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Downsamples 8\(\times\)**} \\ \cline{2-7}  & **RMSE**\(\downarrow\) & **PSNR**\(\uparrow\) & **SSMI**\(\uparrow\) & **ERGAS**\(\downarrow\) & **LPIPS**\(\downarrow\) & **SAM**\(\downarrow\) \\ \hline Bicubic & 83.85 & 19.40 & 0.48 & 3.27 & 0.75 & 14.87 \\ pix2 pix & 61.13 & 21.13 & 0.49 & 3.39 & 0.61 & 15.17 \\ CycleGAN & 176.66 & 12.73 & 0.26 & 3.30 & 0.57 & **14.22** \\ DRIT & 164.49 & 13.64 & 0.25 & 3.27 & 0.53 & 15.00 \\ UNIT & 213.82 & 11.06 & 0.23 & 3.40 & 0.53 & 14.79 \\ SRGAN & / & & & & \\ DSpix2 pix & **57.35** & **23.91** & **0.63** & **3.13** & **0.50** & 14.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of generation effects: DSpix2 pix vs. other networks in 8\(\times\) super-resolution tasks. From the zoomed-in images, the network has a better ability to preserve the content information of the images, with significant retention of hue, terrestrial structures, and other details. This is particularly notable when compared to DRIT and CycleGAN methods. Different super-resolution task samples exhibit consistent experimental results. Additionally, it is evident that the high-resolution images generated by DSpix2 pix exhibit smoother textures and are less affected by noise, resulting in the highest image quality compared to the results from other methods. To further substantiate this observation, we apply Canny edge detection to extract the edges from the 4\(\times\) super-resolution results, as shown in Figure 7. The extracted edges serve to highlight the enhanced smoothness and reduced noise levels in the generated images.