## 3 Experiments and Results
### Real-Data Experiments  
In order to evaluate the reconstruction from satellite images with information hidden by real clouds, new data captured by Landsat 8 were added and pre-processed (according to the steps in Section 3.3). The new data are part of scene 2/67 from September 2023 of the study area (Figure 7). This was chosen due to it being the beginning of spring and the transition from the dry season to the rainy season in the region, according to the National Institute of Meteorology (INMET ([[https://portal.inmet.gov.br/paginas/estacoes](https://portal.inmet.gov.br/paginas/estacoes)]([https://portal.inmet.gov.br/paginas/estacoes](https://portal.inmet.gov.br/paginas/estacoes)), accessed on 13 October 2023)). To delimit the areas with clouds and their shadows, before the reconstruction process, the clouds were segmented using the CFMask algorithm [8]. As a result of the segmentation, the \(m\) mask was generated, which will be considered as one of the inputs for the reconstruction approach, as shown in Figure 2. Figure 16 shows the original image with the presence of real clouds \(x\), the image \(\vec{x^{{}^{\prime}}}=stack(x\odot m,m)\) that results from the image with the mask from the segmentation process stacked with the masked image, the reconstructed image \(\hat{x}\) by [PERSON] and the Big LaMa model and its \(LST\) and \(NDVI\) indices, respectively.  
In order to justify the importance of the reconstruction process for \(LST\) and \(NDVI\) analysis, the indices were calculated for the image with real cloud coverage shown in the first column of Figure 16. The index values for temperature and vegetation are shown in Figure 17.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Image** & **Scenarios** & \(\mathcal{PSNR}\) & \(\mathcal{SSIM}\) & \(\mathcal{RMSE}\) \\ \hline \multirow{6}{*}{LaMa} & \multirow{3}{*}{Reconstructed image} & Thick & 55.0078 & 0.8627 & 0.0118 \\  & & **Medium** & **57.1982** & **0.9181** & 0.0089 \\  & & Thin & 56.4336 & 0.9056 & **0.0075** \\ \cline{2-6}  & & Thick & 43.6098 & 0.6677 & 1.6828 \\  & & Medium & 47.0711 & **0.7994** & 1.1297 \\  & & Thin & **47.5728** & 0.7261 & **1.0663** \\ \cline{2-6}  & & Thick & 69.0568 & 0.7661 & 0.0900 \\  & & Medium & 73.1989 & **0.8622** & 0.0556 \\  & & Thin & **74.3175** & 0.8454 & **0.0489** \\ \hline \hline \end{tabular} The best results are in bold.  
\end{table}
Table 7: LaMa implementation metrics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Image** & **Scenarios** & \(\mathcal{PSNR}\) & \(\mathcal{SSIM}\) & \(\mathcal{RMSE}\) \\ \hline \multirow{6}{*}{Big LaMa} & \multirow{3}{*}{Reconstructed image} & Thick & 55.0588 & 0.8561 & 0.0121 \\  & & **Medium** & **57.1681** & **0.9124** & 0.0079 \\  & & Thin & 56.3385 & 0.8986 & **0.0076** \\ \cline{1-1} \cline{2-6}  & & Thick & 43.6097 & 0.6677 & 2.8321 \\  & & **Medium** & **48.5236** & **0.7729** & **0.9135** \\  & & Thin & 47.7238 & 0.7062 & 1.0982 \\ \cline{1-1} \cline{2-6}  & & Thick & 68.8271 & 0.7613 & 0.0921 \\ \cline{1-1}  & & Medium & 72.6396 & **0.8564** & 0.0591 \\ \cline{1-1}  & & Thin & **73.8074** & 0.8414 & **0.0519** \\ \hline \hline \end{tabular} The best results are in bold.  
\end{table}
Table 8: Big LaMa implementation metrics.