# Satellite Image Compression Guided by Regions of Interest
## 5 Conclusions  
This paper describes a methodology that aims at significant data saving in the transmission of remote sensing satellite images by proposing an on-the-fly analysis to decide which information is effectively useful for specific target applications before compressing and transmitting. As a consequence, bandwidth reduction can be achieved. The proposed system classifies the content as important or not important in order to define regions of interest. Then, the image compressor recommended by the CCSDS is used to compress the ROI with a coding rate \(\alpha\), and the non-important content with a coding rate \(\beta\). To make it possible, the proposed method relies on a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Baseline algorithms for these three detections steps were proposed and formally modeled considering the ForSyDe framework. Then, the algorithms were implemented in Matlab and integrated according to the proposed method to simulate the compression gains. The algorithms were also implemented in Python in order to be executed on the satellite platform iX10-100, aiming at measuring their processing times. Experiments were conducted using 25 images from PlanetScope cropped to a selected area of the Stockholm archipelago, Sweden. The images were selected in order to test the behavior of the system in different situations in both dry-land and water regions. The analyses involved scenarios containing: clouds, cloud shadows, fog, snow/ice, and vessels. Thus, one can evaluate the behavior of the system and observe the strengths and weaknesses of the proposed method. The average gain achieved in case of lossy compressing the regions other than ROI was 0.251, which means that the information to be transmitted was reduced to around 25% of the information required to transmit the images just losslessly compressed. Individually, the gains of each image were in the range of 4.3% to 40.6%. Small improvements to the gains were reached when discarding the regions other than ROI, taking the range of gains to 1.1% to 38.4%, and the average gain to 22.3%. This amount of data saving illustrates the merits of our proposal. We expect that future low-orbit imaging satellites with limited data transmission capacity for specific missions can benefit from this study. The proposed method can also be used in multispectral satellites that work with a larger number of available bands (e.g., PlanetScope SuperDove). In this case, we recommend using the same bands (or bands of similar wavelength) as input to the detection algorithms. Experiments with images from other bands can be carried out in future works. A few weaknesses of the proposed method were observed and discussed, pointing out future directions for the proposed system. First, one can mention the false alarms for cloud detection due to snow/ice on the ground results in lossy compression of the misclassified tiles. To overcome this challenge, the use of more robust cloud detection methodologies can be investigated. Other false alarms observed impact the information savings, but do not compromise the information considered useful. Finally, the need for improvements in the processing time of the vessel detection algorithm can also be mentioned. An implementation aiming at the parallel processing of image pixels in the CFAR normalization step must be performed. In that sense, the automated generation of optimized codes for the target platforms can be pursued considering the design space exploration tools envisioned by ForSyDe. Reductions in processing time can also lead to reductions in energy consumption, since the runtime is an important component in the energy consumption of the embedded platform [2]. Generally, energy consumption measurement is hardware-dependent and relies on how the algorithms are implemented, depending on whether the implementation makes use of parallel processing, GPU processing, and other features.