# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 4 Results and Discussions
### Databases and Experimental Parameter Setting  
In this work, we use three different satellite data sets, namely, Kaggle open source dataset [41], AID [42] and WHU-RS19 [43]. For the Kaggle open source dataset, it has 1720 satellite images with size of 3099 \(\times\) 2329 pixels. First of all, in the training stage, we crop images into 720 \(\times\) 720 patches to increase the amount of data. Then the input LR  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**UDSN** & **EESN** &
\begin{tabular}{c} **Edge-** \\ **Extraction** \\ \end{tabular} & \(L_{cont}\) & \(L_{adv}\) & \(L_{img\_est}\) & \(L_{edge\_est}\) & \(L_{perc}\) & \(L_{edge\_perc}\) & **PSNR** & **SSIM** & **FSIM** \\ \hline
2D-RRDB & 2D-RDB & Laplacian & & & & & & & & 32.676 & 0.890 & 0.986 \\
2D-RRDB & RRDB & Laplacian & & & & & & & & 32.935 & 0.910 & 0.989 \\
2D-RRDB & 2D-RRDB & Laplacian & & & & & & & & 32.947 & 0.913 & 0.990 \\
2D-RRDB & 2D-RRDB & Sobel & & & & & & & & 32.950 & 0.906 & 0.984 \\
2D-RRDB & 2D-RRDB & Canny & & & & & & & & 33.011 & 0.918 & 0.994 \\
2D-RRDB & 2D-RRDB & Canny & & & & & & & & 33.057 & 0.923 & 0.990 \\
2D-RRDB & 2D-RRDB & Canny & & & & & & & & 33.103 & 0.925 & 0.987 \\
2D-RRDB & 2D-RRDB & Canny & & & & & & & & 33.081 & 0.917 & 0.994 \\
2D-RRDB & 2D-RRDB & Canny & & & & & & & & & 33.111 & 0.921 & 0.990 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 6: Network architecture of the discriminator.  
image has a size of 180 \(\times\) 180, which is obtained by down-sampling HR images using the MATLAB bicubic kernel function, which follows most existing satellite image super-resolution methods [6; 19] (i.e., using a simple down-sampling model with a fixed kernel to create training images). In total, the dataset has 20,640 images.  
Regarding the AID dataset [42], it contains 10,000 images in total with size of 600 \(\times\) 600 and consists of 30 scene types. In addition, for the training stage of above two data sets, 80% of samples are chosen as the training set and the rest 20% of samples are treated as the test set, where the training set is divided into 90% for training and 10% for validation.  
For the WHU-RS19 dataset [43], the image size of this dataset is 600 \(\times\) 600, which contains a total of 1005 satellite images of 19 categories. In this dataset, we use all the images as the test data and the test model is trained on the Kaggle open source dataset.  
Moreover, in order to prove that the method we proposed is effective in real scenarios, we will conduct experiments for the data set collected by SpaceWill [44]. Here we directly input the LR test image into the network trained with Kaggle, where eight scenes were selected as test samples, including the Petronas Twin Towers in Malaysia, the Pentagon Tower in the United States, the Forbidden City in Beijing, the Dubai Tower, the Beijing Olympic Pavilion, the Hong Kong Convention Center, the Saifuding Mosque in Brunei, and the Potala Palace in Qinghai-Tibet. For convenience, we cropped them into a uniform size of 1000 \(\times\) 1000 pixels for testing.  
For all conducted experiments, the Adam optimizer is used in the training process and we set the parameter \(\beta_{1}\) to be 0.9. We also set the initial learning rate to be \(2\times 10^{-4}\). The generator and discriminator are trained alternately until our model converges.