# Satellite Image Compression Guided by Regions of Interest
## 3 Results
Variations were observed both in the number of tiles marked as containing changes on dry land and in the number of tiles marked as containing vessels in water regions, resulting in gains between 0.155 and 0.263 for \(\beta=0.2\). The exception is observed for image 8, in which 56 tiles were marked as ROI: 35 tiles were marked as containing changes in dry land and 21 tiles were marked as containing vessels in water regions. The increase in the number of tiles marked in dry land was a result of deviations between the surveillance image and the reference image due to image corrections and calibrations. In fact, the results for image 8 illustrate possible consequences of deviations in georeferencing, differences in sensors calibrations, or failure of other corrections to the images. Another point that can be observed concerns the border between dry-land and water regions. In this paper, the tiles containing partially dry land and partially water regions were marked as dry land. In turn, such tiles are processed by the change detection algorithm (i.e., not by the vessel detection algorithm). This choice was made because the change detection algorithm can detect some vessels in the water region as changes if the reference image does not contain the same vessel in the same position, while the vessel detection algorithm is not able to detect changes in the dry-land region. In a real application in the future, the proposed method can be adjusted to allow the execution of both algorithms if the application requires more accurate vessel detection in these tiles. The results of image 5 are brought by Figure 10 and can be used to illustrate that issue. Figure 10 shows image 5, in which many vessels can be observed in the water region. One of them is located near a small island around the tile F5, which was defined as dry land because it contains part of the island. Figure 10 shows detection in tile F5, close to the tile G5, and as a consequence, tile F5 was labeled as containing changes by the system, as shown by Figure 10.  
Figure 9: Results for image 2: (**a**) RGB image (truncated at 2000); (**b**) labeled tiles (truncated at 4000); (**c**) compressed NIR band (truncated at 4000). The system's behavior in scenarios containing clouds can also be discussed. Initially, some comments can be made by analyzing the results achieved with image 9. Figure 10(a) shows the RGB-bands image of image 9, where it is possible to observe the cloud portion mostly in the lower-left quadrant. In addition, small portions of clouds can be found over the water region. The shadows of the clouds on the ground can be observed in both dry-land and water regions. Figure 10(c) shows the tiles labeled by the system. Tiles labeled as \"Cloudy\" can be found in the lower-left quadrant of Figure 10(c). This is the case for tiles A1, B1, A2, B2, C2, A3, B3, C3, D3, B4, and D4. The other tiles with clouds in dry land were wrongly labeled as containing changes. The reason for this is: (1) some parts of the clouds into the tiles are not sufficiently bright to cross the threshold \(\tau_{1}\) (e.g., tile A4), or (2) the tiles are not sufficiently covered by clouds to cross the threshold \(\tau_{2}\) (e.g., tile B6). As mentioned in Section 2, the tiles wrongly classified as \"No-clouds\" can be marked as ROI by the change detection algorithm. The real changes detected in dry land are similar to those detected in image 5, which are located at the upper-left quadrant. In addition, tiles labeled as containing  
Figure 11: Results for image 9: (**a**) RGB image (truncated at 2000); (**b**) binary image; (**c**) labeled tiles (truncated at 4000). Figure 10: Results for image 5: (**a**) RGB image (truncated at 2000); (**b**) binary image; (**c**) labeled tiles (truncated at 4000). changes due to the shadows of the clouds can also be observed. This is the case for tiles A8, B8, and C8, for instance.