## 3 Experiments and Results
### Simulated Data Experiments
The figure also shows _LST_ and _NDVI_ results calculated from the reconstructed image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Epochs** & **PSNR** & **SSIM** & **RMSE** \\ \hline \multirow{4}{*}{Big LaMA} & 32 & 58.2209 & 0.7647 & 0.4593 \\  & 35 & 58.7348 & 0.7702 & 0.4474 \\ \cline{1-1}  & **38** & **58.8912** & **0.7725** & **0.4381** \\ \cline{1-1}  & 39 & 51.5999 & 0.7479 & 0.8512 \\ \hline \hline \end{tabular} The best results are in bold.  
\end{table} 
Table 6: Metrics achieved for the _LST_ during training using the Big LaMA model. Figure 11: The ground truth image and its respective _LST_ and _NDVI_. Figure 12: Reconstruction of satellite images contaminated by synthetic clouds using the LaMa network. Each row shows images corresponding to a specific scenario and each column shows images with synthetic clouds, the reconstructed image and its _LST_ and _NDVI_, respectively. Figure 13: Reconstruction of satellite images contaminated by synthetic clouds with the Big LaMa model. Each row shows images corresponding to a specific scenario and each column shows images with synthetic clouds, the reconstructed image and its _LST_ and _NDVI_, respectively. of each figure show the behavior of the reconstructed image and its _LST_ and _NDVI_. The second and third lines correspond to the results of scenarios 2 and 3, respectively. The plots consider the comparison between two variables, the average of the three bands that make up the original image without synthetic clouds and the average of the three bands that make up the reconstructed images. The resulting regions of dispersion show that the recovery carried out by the Big Lama model is clearly more concentrated, which means a higher level of consistency and precision in the reconstruction of missing areas, further highlighting its superior performance. Also, with Big Lama there is a greater strength of relationship between the variables and a higher coefficient of determination for scenario 1, representing the greater accuracy of the regression equation. For the quantitative analysis in the reconstruction of the example patch, _PSNR_, _SSIM_ and _RMSE_ metrics were calculated with a normalization of the reconstructed image's data and its _LST_ and _NDVI_. Tables 7 and 8 show the results obtained by the LaMa network and the Big LaMa model in the cloud-removal task, for the Landsat data shown in Figures 12 and 13. Figure 14: LaMa network scatter plots. The black straight line represents the original image, while the red line represents the analysis of the reconstructed image versus the original image for three different scenarios. Each row corresponds to a specific scenario and each column corresponds to the reconstructed image and its _LST_ and _NDVI_, respectively. From the quantitative results shown in the tables, it can be seen that, when it comes to image reconstruction and considering its visual quality as measured by the _SSIM_ metric, the LaMa and Big Lama networks obtained better results in scenario 2, which considers medium-sized synthetic cloud cover. When it came to measuring the reconstruction of content losses, _PSNR_ and _RMSE_ indicators, the obtained values generally indicated a better behavior of the reconstructed image in scenario 3, which considers the coverage of synthetic clouds with thin areas. It can also be seen that there are some cases wherein better behavior is observed in scenario 2. In the reconstructed image, the best _PSNR_ value is achieved in scenario 2 with a value of 57.1982, the best _SSIM_ with a value of 0.9181 also for scenario 2, while the _RMSE_ reaches 0.0075 as the best result in scenario 3. All the metrics were achieved using the LaMa network reconstruction. When the _LST_ was analyzed, the best _PSNR_ value was achieved in scenario 3 with a value of 48.5236 using the Big LaMa model. The best SSIM result was 0.7994 for scenario 2 using the LaMa network. The _RMSE_ reached 0.9135 as the best result for scenario 3 using the Big LaMa model.