# Satellite Image Compression Guided by Regions of Interest
## 4 Discussion  
The average gains for the 25 images were 0.251 (25%) for \(\beta=0.2\) and 0.223 (22.3%) for \(\beta=0\). The individual gains in terms of percentage achieved for the 25 images with the application of the proposed method can be compared in Figure 19.  
The best gains observed were for image 0, reaching around 4.3% for \(\beta=0.2\) and 1.1% for \(\beta=0.2\). As mentioned in Section 3, image 0 is a case of no changes in the dryland region and two tiles containing possible vessels in the water region. That result can be considered compatible with scenarios of entirely images of water regions containing few vessels. If all the tiles of image 0 are not marked as ROI, and consequently the whole image 0 is lossy compressed with encoding rate \(\beta=0.2\), the gain would be 0.030. This is around 3% of the information needed by the whole losslessly compressed image. This can be considered the case for ocean images without vessels, for instance. On the other hand, the images will be entirely losslessly compressed in dry-land areas, having changes in all of their tiles,  
Figure 19: Percentage gains for the evaluated images. Figure 18: Labeled tiles of images 23 and 24 (NIR band truncated at 4000): (**a**) image 23; (**b**) image 24.  
which indicates that no information will be saved, resulting in gains equal to 1. The same goes for images entirely in water regions containing vessels in all of their tiles. In this sense, from 2 to 58 tiles were marked as ROI for the images evaluated, within a range of possible amount of tiles from 0 to a maximum of 169. Estimates of gains for the other possible number of tiles can be performed through a linear curve fitting. Figure 20 shows the estimated curves of gains for \(\beta=0.2\) and \(\beta=0\), together with the gains achieved for each image (the dots round its respective curves). The behavior of both curves needs to be the same for 169 tiles, as there is no information being saved in this situation (i.e., gains of 100%). On the other hand, when the number of tiles as ROI is equal to zero, the gain for \(\beta=0\) is also zero, indicating that all information is being discarded. For \(\beta=0\), zero tiles means that all the tiles are lossily compressed, so the gain must be greater than zero. The curves should better fit this theoretical behavior for a larger number of images. To discuss the weaknesses of the proposed method, the following needs to be considered. In general, detection algorithms present a trade-off between PD and FAR, in which maximizing the PD generally implies an increase in the number of false alarms. This can be considered a multi-objective optimization problem. Thus, the application rules the balancing point between the two. For the proposed method described in this paper, the policy is to ensure the transmission of all important content. Consequently, the PD of the change detection algorithm should be as high as possible, since false alarms on change detection results in unimportant content marked as ROI, and not the opposite. The same is true for the vessel detection algorithm. Thus, the parameters of both algorithms were chosen to prioritize the PD. However, the same is not valid for the cloud detection algorithm. False alarms can cause loss of important content (e.g., a tile containing snow), whereas an undetected cloud can lead unimportant content to be marked as ROI. In turn, the choice of parameters for the cloud detection algorithm should prioritize the minimization of false alarms, even if this leads to a decrease in the PD. As discussed in [27], cloud detection is a challenge for cloud and snow coexisting areas, since both have similar spectral characteristics in the visible spectrum. To overcome this challenge, some methodologies based on machine learning have been proposed. Most of them focus on exploring textures and other image features to improve the accuracy of automated methods. However, some machine learning models for cloud detection are large, which limits their applicability and explainability.