Distributed On-Orbit Sparse Coding for Efficient Space Situational Awareness Image Transmission

Space Situational Awareness (SSA) relies on Low Earth Orbit (LEO) satellites to capture continuous, high-resolution imagery critical for identifying space threats. The vast volume of SSA images overwhelms satellite network band-width, hindering timely transmission and processing. This paper presents...

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Vydané v:Annual Joint Conference of the IEEE Computer and Communications Societies s. 1 - 10
Hlavní autori: Liu, Yutong, Jin, Haiming, Yao, Yinjie Wang, Chen, Yunxiang, Zhao, Yimin, Kong, Linghe, Li, Rui, Liu, Xiaoyang, Chen, Guihai
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 19.05.2025
Predmet:
Arithmetic
Data communication
Dictionaries
distributed on-orbit computing
Image coding
Image communication
image compression
Low earth orbit satellites
Machine learning
multi-view sparse coding
Redundancy
Satellites
Space Situational Awareness
Space vehicles
ISSN:2641-9874
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Shrnutí:Space Situational Awareness (SSA) relies on Low Earth Orbit (LEO) satellites to capture continuous, high-resolution imagery critical for identifying space threats. The vast volume of SSA images overwhelms satellite network band-width, hindering timely transmission and processing. This paper presents a novel image compression method based on sparse coding to mitigate this transmission bottleneck. By exploiting the high sparsity and spatial-temporal redundancy of SSA images, we introduce an Aggregated Dictionary Learning (ADL) algorithm and a Context-aware Adaptive Binary Arithmetic Coding (OABAC) algorithm for further reducing dictionary and coefficient sizes. The proposed sparse coding is operated across LEO satellites in a distributed manner. Both overlapping and non-overlapping regions of the image are divided and processed paralleled on different satellites, optimizing resource usage and reducing latency. Evaluations show a 93.78% high compression ratio, surpassing existing methods and ensuring efficient SSA data transmission and processing in constrained satellite networks.
ISSN:2641-9874
DOI:10.1109/INFOCOM55648.2025.11044504