An Elastic Coding and Decoding Method for Satellite Remote Sensing Image Semantic Transmission

Remote sensing images play a crucial and indispensable role in many fields such as environmental monitoring and geological disaster detection. With the advancement of satellite remote sensing acquisition technology, the remote sensing data shows explosive growth. However, the current communication b...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on network science and engineering pp. 1 - 14
Main Authors: Zhang, Zhongqiang, Zhang, Shuhang, Li, Haoyong, Shi, Guangming, Xue, Jiayin, Li, Bin
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Channel coding
Decoding
Deep learning
elastic coding and decoding
Feature extraction
global-local feature extraction module
Image coding
Image communication
key semantic feature selection module
Remote sensing
Remote sensing image semantic transmission
Semantic communication
Signal to noise ratio
Wireless communication
ISSN:2327-4697, 2334-329X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Remote sensing images play a crucial and indispensable role in many fields such as environmental monitoring and geological disaster detection. With the advancement of satellite remote sensing acquisition technology, the remote sensing data shows explosive growth. However, the current communication bandwidth of the satellite-to-ground channel is difficult to meet the requirements for massive remote sensing data transmission. To this end, this paper proposes an elastic coding and decoding method for remote sensing image semantic transmission. The proposed transmission method includes a global-local feature extraction module, a key semantic feature selection module, a joint source channel coding module, a decoding module, and an analysis module. The global-local feature extraction module can effectively extract global context features and local detailed features via multi-directional mamba block and residual block, respectively. The key semantic feature selection module can elastically select key features according to channel state signal-to-noise ratios (SNRs). The joint source channel coding and decoding modules can further improve the transmission robustness via adding different types of channel conditions. The proposed method only needs to transmit key information in remote sensing images while discarding the redundant information, which significantly improves the transmission efficiency of remote sensing images. The extensive experimental results on the NWPU-RESISC45, UCMerced-LandUse, AID, and RSSCN7 datasets demonstrate that our method obtains higher transmission accuracies and transmission efficiency than state-of-the-art methods.
ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2025.3632547