INFOCOM 2025 · acceptance 18.66%
Distributed On-Orbit Sparse Coding for Efficient Space Situational Awareness Image Transmission
Yutong Liu , Haiming Jin , Yunxiang Chen, Wangyinjie Yao, Yimin Zhao , Linghe Kong , Lei Dong, Rui Li, Xiaoyang Liu, Guihai Chen
Abstract
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 bandwidth, 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 in parallel 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.
Cite (BibTeX)
@inproceedings{liu2025infocom,
title = {Distributed On-Orbit Sparse Coding for Efficient Space Situational Awareness Image Transmission},
author = {Liu, Yutong and Jin, Haiming and Chen, Yunxiang and Yao, Wangyinjie and Zhao, Yimin and Kong, Linghe and Dong, Lei and Li, Rui and Liu, Xiaoyang and Chen, Guihai},
booktitle = {IEEE INFOCOM 2025 - IEEE Conference on Computer Communications},
year = {2025}
}