As space technology advances, launching low Earth orbit (LEO) satellites become easier and LEO satellites are being used in various fields. In particular, LEO synthetic aperture radar (SAR) system is in the spotlight with many advantages, e.g., regardless of weather condition, 24 hour operation. SAR system can be used in various fields such as object detection and disaster observation. However, SAR image has speckling noise, so image pre-processing must be required. There are many researches on the SAR image processing, however, few publications are considering a buffer status. Therefore, in this paper, we suggest the optimal SAR image pre-processing in LEO SAR satellites and a ground station with finite buffer based on deep reinforcement learning (DRL). As a result of DRL simulation, while changing the buffer size of the LEO SAR satellites, efficiency of buffer was improved by selecting the optimal filter size according to the state of the buffer.
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1A4A1030775) and by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2022-2018-0-01424) supervised by the IITP(Institute for Information & communications Technology Promotion)
