Adaptive Semantic Communication System for High-Quality Remote Sensing Image Transmission in Unstable Wireless Environments

doi:10.1109/TWC.2025.3600880

CSpace

	Adaptive Semantic Communication System for High-Quality Remote Sensing Image Transmission in Unstable Wireless Environments
	Tan, Zhangyayu 1,2; Liu, Caiping 1,2; Xie, Kun 1,2; Ouyang, Yudian 1,2; Wen, Jigang 3; Zhang, Guangxing 4; Chen, Dong 5; Xie, Gaogang 6; Li, Kenli 1,2
	2026
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
ISSN	1536-1276
卷号	25 页码:3001-3015
摘要	High-quality remote sensing imagery plays a vital role in environmental monitoring and disaster management. However, transmitting these images is challenging due to the unstable signal-to-noise ratio (SNR) and bandwidth limitations encountered in remote communications. Semantic communication, particularly deep learning-based methods, offers a promising solution by jointly optimizing source and channel coding to achieve data compression and noise resilience. Nevertheless, existing methods struggle to cope with varying channel noise and bandwidth, leading to unsatisfactory image reconstruction quality. To address these challenges, we propose a satellite-ground compression and transmission system called Adaptive Residual Joint Source-Channel Coding (ARJSCC), which is based on Deep Joint Source-Channel Coding (DeepJSCC). The ARJSCC system compresses remote sensing images into semantic information and residuals to achieve low overhead transmission and high-quality reconstruction. ARJSCC utilizes an attention module to adjust the semantic preference of the model for different SNRs, and deploys a variance-based position mask module to flexibly vary the semantic length and further compress it. These designs enable ARJSCC to automatically adapt to varying noise and bandwidth conditions. Moreover, for the residual, we apply BPG to compress it to reduce the transmission cost and design the corresponding enhancement module to recover its details from the noise-affected compressed residual. We experimentally compare our ARJSCC with the recent DeepJSCC-based wireless image transmission models in low-resolution dataset and high-resolution remote dataset under multiple wireless channel environments. The experimental results show that ARJSCC can achieve high reconstruction quality exceeding 44dB, and outperform the competitors by 4-6db even under low SNR and bandwidth environments.
关键词	Semantics Image coding Bandwidth Image reconstruction Signal to noise ratio Wireless communication Adaptation models Wireless sensor networks Remote sensing Training Joint source-channel coding (JSCC) semantic compress semantic communication wireless image transmission
DOI	10.1109/TWC.2025.3600880
收录类别	SCI
语种	英语
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001659563700033
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42906
专题	中国科学院计算技术研究所
通讯作者	Xie, Kun
作者单位	1.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410012, Peoples R China 2.Minist Educ, Key Lab Fus Comp Supercomp & Artificial Intelligen, Changsha 410006, Peoples R China 3.Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411201, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Dept Network Technol Res Ctr, Beijing 100864, Peoples R China 5.China Mobile Commun Grp Co Ltd, Beijing 100032, Peoples R China 6.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Tan, Zhangyayu,Liu, Caiping,Xie, Kun,et al. Adaptive Semantic Communication System for High-Quality Remote Sensing Image Transmission in Unstable Wireless Environments[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2026,25:3001-3015.
APA	Tan, Zhangyayu.,Liu, Caiping.,Xie, Kun.,Ouyang, Yudian.,Wen, Jigang.,...&Li, Kenli.(2026).Adaptive Semantic Communication System for High-Quality Remote Sensing Image Transmission in Unstable Wireless Environments.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,25,3001-3015.
MLA	Tan, Zhangyayu,et al."Adaptive Semantic Communication System for High-Quality Remote Sensing Image Transmission in Unstable Wireless Environments".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 25(2026):3001-3015.