# Satellite Image Compression Guided by Regions of Interest
## 2 Materials and Methods  
The proposed method relies on a pre-processing step before coding and transmitting the acquired images. The focus is on remote sensing satellite applications that agree with the following: (1) changes in dry-land regions other than clouds are considered as ROI; (2) water regions containing vessels are considered as ROI. Other content is considered as not useful. Thus, let \(r\) be the coding rate of the image compressor. The ROI can be compressed with a coding rate \(r=\alpha\), and the other content can be compressed with a coding rate \(r=\beta\), for \(\beta<\alpha\). As mentioned in Section 1, the proposed method makes use of a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. The algorithm's workflow is shown in Figure 1.  
First, the input image is segmented into tiles of \(\delta\) by \(\delta\) and processed by the cloud detection algorithm. The tiles classified as containing clouds (labeled as \"Cloudy\") are compressed with the coding rate \(r=\beta\). The other tiles (labeled as \"No-cloud\") are processed by the change detection algorithm if they contain dry-land regions. The differentiation between dry-land and water regions by georeferencing is considered in this paper. For  
Figure 1: Diagram of the pre-processing step. The image (tile) compression with the coding rate \(\alpha\) is represented by the green circles, and the compression with the coding rate \(\beta\) is represented by the red circles. The algorithms for cloud detection, change detection, and vessel detection are illustrated by the yellow blocks. water regions, the \"No-cloud\" tiles can be processed by the vessel detection algorithm. Alternatively, the tiles located in water regions can be processed by the change detection algorithm, in case the target application is interested in any other changes (e.g., oil spill or icebergs) in addition to the vessels, which can also be considered as a change. In this case, the reference image used by the change detection algorithm can be, for instance, a synthetic image which best characterizes the water region or oceanic area. The option of using the change detection algorithm instead of the vessel detection algorithm is represented by the switch S1. The ability of the change detection algorithm to detect vessels as changes must be taken into account when setting S1. The use of the vessel detection algorithm may be preferable, as cloud detection algorithms generally can not ensure a 100% detection rate. Consequently, the tiles wrongly classified as \"No-clouds\" will be marked as ROI by the change detection algorithm, which can reduce the performance of the system. Ideally, that problem can be solved by using the vessel detection algorithm, since detecting vessels in cloudy images should result in \"No-vessels\". However, the cloud detection step is still necessary, considering that the presence of clouds increases the number of false alarms produced by the vessel detection algorithm. In addition, the computational costs for vessel detection and change detection are different, varying according to the implementation of the algorithms (including optimizations) and the restrictions imposed by the platform. These issues can also be considered when setting S1 while aiming at processing time and energy savings. Finally, the tiles containing changes or vessels are compressed with the coding rate \(r=\alpha\), and the other tiles are compressed with the coding rate \(r=\beta\). In this paper, baseline algorithms for the cloud detection, change detection, and vessel detection steps are proposed. The proposed method was expected to show good results, even compared with non-learning-based algorithms. However, the use of other algorithms that satisfy the constraints presented by the embedded platform can also be considered. The baseline algorithms were modeled using ForSyDe [13], which is a methodology with a formal basis for modeling and design of heterogeneous systems-on-chip and cyber-physical systems.