# Satellite Image Compression Guided by Regions of Interest
## 3 Results
Most of the fog is over the water region in image 18. Thus, the fog is not affecting much of the change detection in the dry-land region. The damages are basically in tiles C13, D13, D12, E12, and D11, where the differences from the reference image exceed the threshold \(\tau_{3}\). The other tiles marked as ROI on dry land can be considered the result of very small and punctual changes. Differently, the fog greatly compromised the detection of changes in image 20, in which almost all the tiles were marked as ROI in Figure 16c. The exception are tiles E12 and D3, the latter being eliminated by the cloud detection algorithm. For the water region, the results of image 18 reveal that most of the vessels were detected even with the fog. The tiles containing vessels but not marked as ROI exist because fog reduces the contrast between the water and the vessel, changing the pixels' statistic in the CFAR normalization filter window. This was the case for tiles M6 and G7, for instance. Thus, some pixels related to small vessels ended up not reaching the threshold \(\tau_{4}\). In the case of image 20, all vessels that could be perceived were detected. As shown by Figure 16a, two vessels can be seen at the top-right quadrant, resulting in three tiles marked as ROI in Figure 16c, as one of them is located on the boundary between tiles J12 and K12. Figure 7:2 Results for image 20: (**a**) RGB image (truncated at 2000); (**b**) binary image; (**c**) labeled tiles (truncated at 4000). Figure 7:3 Results for image 18: (**a**) RGB image (truncated at 2000); (**b**) binary image; (**c**) labeled tiles (truncated at 4000). The behavior of the proposed method with images containing snow and ice can be initially discussed through Figure 17, which shows the results of image 21. Image 21 is a scenario with both snow and clouds. Figure 17a shows the RGB-bands image of image 21 with the maximum value truncated at 2000, in which the bright pixels resulting from both snow and clouds can be observed. Considering that \(\tau_{1}=1500\), detections from both sources can be equally perceived by the cloud detection algorithm. However, the dry-land tiles were not wrongly classified as cloudy, as shown by Figure 17c. That is because in the case of image 21, the volume of snow in the area was not sufficient to exceed threshold \(\tau_{2}\) due to vegetation and terrain topography. The tiles of dry-land region that are not covered by clouds were marked as ROI by the change detection algorithm. Figure 17b shows that all the pixels of the tiles processed by the change detection algorithm were marked because they reached threshold \(\tau_{3}\). In the water region, the vessel detection algorithm produced seven false alarms due to clouds. Overall, the gains for image 21 were 0.237 for \(\beta=0.2\) and 0.205 for \(\beta=0\). The gains achieved with image 22 were 0.372 for \(\beta=0.2\) and 0.351 for \(\beta=0\), which are less attractive than those achieved with image 21. The amount of snow in image 22 is less than that in image 21, but also distributed over the entire dry-land area. The difference in the gain values of the two images occurred because almost all tiles were classified as containing changes in image 22, as there were no clouds over the dry-land region. The other characteristics presented by both images are similar. The results for images 23 and 24 can be used to describe some undesirable outcomes. Figure 18 shows the labeled tiles for both images. Differently from image 21, image 23 has no clouds, but 33 tiles in dry-land region were marked as \"Cloudy\", as shown by Figure 18a. This is because both \(\tau_{1}\) and \(\tau_{2}\) were reached due to snow. As a consequence, most tiles with snow were lossily compressed, which may be considered undesirable for some applications. For image 24, four tiles were wrongly lossily compressed on dry land due to snow: tiles A4, A5, A10, and A11. In turn, the gains of image 23 are better than those of image 24; however, the distortions of image 23 are greater.