## 3 Experiments and Results
### Simulated Data Experiments  
The PAmazon-Cloud database, described in Section 3.3, is used to carry out the approach experiments. The dataset consists of images captured by Landsat-8, duly pre-processed. The training run was limited to forty epochs and took a total of 12 days on a personal computer with characteristics detailed in Table 3. Specifically, the training process used the _SSIM_ as an error metric to conduct the iterative adjustment to obtain the image reconstruction; the results achieved for LaMa and Big LaMa are shown in Table 4. In regard to reconstruction, it can be seen that the LaMa network and the Big LaMa model obtained their best results at epoch thirty-five, with values of 0.9802 and 0.9799, respectively. During the experiments, the algorithm was subjected to three types of metrics in order to evaluate performance and measure the visual quality of the image reconstruction. In the training process, the metrics were calculated for the _LST_ due to its greater representativeness, the values of which are shown in Tables 5 and 6. It can be seen that the LaMa network achieved the best results at epoch thirty-five, with _PSNR_ values of 55.9807, _SSIM_ of 0.7728 and _RMSE_ of 0.4253. For the Big LaMa model, with thirty-eight epochs, the best _PSNR_, _SSIM_ and _RMSE_ values were 58.8912, 0.7725 and 0.1920, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Epochs** & _SSIM_ \\ \hline \multirow{4}{*}{LaMA} & 19 & 0.9780 \\  & 32 & 0.9793 \\  & **35** & **0.9802** \\  & 39 & 0.9788 \\ \hline \multirow{4}{*}{Big LaMA} & 19 & 0.9776 \\  & 32 & 0.9791 \\ \cline{1-1}  & 35 & **0.9799** \\ \cline{1-1}  & 39 & 0.9767 \\ \hline \hline \end{tabular} The best results are in bold.  
\end{table} 
Table 4: Metrics achieved during training. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hardware** & RAM & CPU & GPU \\
32 GB & Intel Xeon W-2123 & NVIDIA Quadro P2000 \\ \hline \multirow{2}{*}{**Software**} & Python & CUDA & PyTorch \\  & 3.6.13 & 10.2 & 1.10.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hardware configuration and the software environment. The applicability of the approach is under in three scenarios:  
* Scenario 1: The image \(x\) is contaminated with a random thick mask \(m\) and fixes multiple thick areas to be reconstructed. * Scenario 2: In this scenario, the input image \(x\) is subjected to a random medium mask \(m\) that covers multiple medium-sized areas of reconstruction. * Scenario 3: For this scenario, the input image \(x\) is subjected to a random thin mask \(m\) that covers multiple thin areas of reconstruction. Figure 11 shows an image without cloud cover, which is considered the original reference image for the experiments. In the same figure, one can see their respective calculated _LST_ and _NDVI_, which represent the temperature and vegetation characteristics present in the reference image. For comparison purposes, the ground truth image was considered throughout the image reconstruction process presented in this paper. In order to carry out the experiments within scenarios 1 to 3 in a clear and cohesive manner, the respective mask was added to an example patch for reconstruction. Figures 12 and 13 show the visual results of recovery using LaMa and Big LaMa, respectively, for each case described by the scenarios. The first line of Figure 12 shows the result of the reconstruction in the missing areas delimited by the thick mask, characterizing scenario 1. The _LST_ and _NDVI_ were calculated from the reconstructed image. The second line shows the reconstruction from medium masks, scenario 2, and their respective _LST_ and _NDVI_ indices. The third and final line shows the reconstruction with thin masks in addition to the calculated _LST_ and _NDVI_. The entire process here described was carried out using the LaMa network. Figure 13 shows results from the Big LaMa model. Line 1, 2 and 3 of the figure show reconstructions corresponding to scenarios 1, 2 and 3, respectively.