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Deep Reinforcement Learning Based Adaptive Synthetic Aperture Radar Image Filtering Algorithm
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Publication Year
2023-02-01
Journal
Journal of Korean Institute of Communications and Information Sciences
Publisher
Korean Institute of Communications and Information Sciences
Citation
Journal of Korean Institute of Communications and Information Sciences, Vol.48 No.2, pp.172-175
Keyword
Buffer StateDeep Reinforcement Learning (DRL)Low Earth Orbit (LEO)Speckle Image FilteringSynthetic Aperture Radar (SAR)
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsInformation Systems and ManagementComputer Science (miscellaneous)
Abstract
Low Earth orbit (LEO) satellite synthetic aperture radar (SAR) is an efficient technology for Earth observation, and a lot of research is in progress. However, due to the time-limited property of LEO satellites, research on time-efficient SAR image processing is essential. In this paper, we propose an adaptive speckle noise filtering algorithm based on deep reinforcement learning for efficient processing of LEO SAR images. The proposed algorithm adaptively selects the filter size according to the buffer state to derive the maximum image resolution in a limited time. As a result of the simulation, the proposed algorithm selects the filter size more efficiently according to the buffer state than the method of conventional algorithm.
ISSN
2287-3880
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/34067
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85189163674&origin=inward
DOI
https://doi.org/2-s2.0-85189163674
Journal URL
https://engjournal.kics.or.kr/digital-library/38352
Type
Article
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Jung, Soyi Image
Jung, Soyi정소이
Department of Electrical and Computer Engineering
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