## 3 Experiments and Results
### Data Pre-Processing and Dataset Creation  
Four pre-processing steps were required on the dataset in order to optimize image processing. The first stage consisted of merging the bands needed to measure \(LST\) and \(NDVI\). The second stage involved aligning and cutting out the areas with no data. In the third stage, the image was cut into smaller patches. Finally, in the fourth and last stage, data augmentation was carried out.  
In the first stage, the bands used to calculate \(NDVI\) and \(LST\) were stacked, i.e., the B4 (Red), B5 (Near Infrared--NIR) and B10 (Thermal Infrared--TIRS) bands became a single image with three channels. Figure 8 illustrates this process.  
In the second stage, four steps were carried out to deskew the satellite images. (i) The Hough transform was used to locate the \(\theta\) tilt angle of the images. The Hough transform [33] is a practical method for linking pixels globally and detecting curves where, from a binarization of the image, subdivisions of the \(\rho\theta\) plane are specified; then, the count of accumulator cells is examined for high concentrations of pixels and finally the continuity  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenes** & **Number of Acquired Images** & **Number of Images/Year** \\ \hline
1/67 & 7 & 2(2021), 2(2022), 3(2023) \\
2/66 & 3 & 1(2021), 1(2022), 1(2023) \\
2/67 & 5 & 3(2021), 2(2023) \\
3/66 & 3 & 2(2022), 1(2023) \\
3/67 & 5 & 3(2022), 2(2023) \\
4/66 & 1 & 1(2022) \\
4/65 & 1 & 1(2022) \\
5/66 & 1 & 1(2022) \\ \hline Total: 8 & Total: 26 & Total: 26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information on the images acquired per scene.  
Figure 8: Process of stacking the B4, B5 and B10 bands to generate a single image.  
relationship between the pixels of the chosen cell is examined, thus making it possible to determine the \(\theta\) tilt angle of the images. (ii) The image was rotated according to the angle found. (iii) The edge of the image was detected using the Canny function. The Canny approach [34] is based on three basic objectives: 1. Low error rate. All edges should be found as close as possible to the true edges; 2. The edge points should be well located. That is, the distance between a point marked as an edge by the detector and the center of the true edge must be minimal; 3. Response from a single edge point. The number of local maxima around the true edge must be minimal. This means that the detector should not identify multiple-edge pixels where only a single edge point exists. (iv) The spare region of the image was removed using the detected edge. This process is illustrated in Figure 9.  
In the third stage, the images were patched in order to reduce the computational cost and enable the network to learn on the available hardware. The patches were set to a size of 256 \(\times\) 256 pixels, so for a captured image of 7800 \(\times\) 7600 pixels, the standard size for Landsat 8 scenes, 573 patches were obtained. Thus, from the 26 images acquired, a total of 14,898 image patches were obtained. Also, 573 patches were generated from the only image with clouds for further experiments. Figure 10 illustrates this process.  
The fourth and final stage consist of data augmentation, in which horizontal and vertical mirroring was performed on the patches made in stage tree, resulting in 44,646 patches, generating the dataset called PAmazon-Cloud.  
The PAmazon-Cloud images were divided into three sets, one with 80% (40,646), and the other two with 10% (2000), forming the training, validation and test datasets, respectively.