面向无人机航拍图像传输的信道弹性编解码方法

张中强; 邱奇; 苗雅杰; 黎斌; 王野; 石光明

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面向无人机航拍图像传输的信道弹性编解码方法

更新时间：2026-03-03

- 面向无人机航拍图像传输的信道弹性编解码方法
- A channel elastic encoding and decoding method for unmanned aerial vehicle image transmission
- 物联网学报 2026年
- 作者机构：
  
  1.鹏城实验室，广东深圳 518055
  2.哈尔滨工业大学（深圳），广东深圳 518055
- 作者简介：
  
  邱奇，qqq.qiu.qi@gmail.com
- 基金信息：
  
  国家自然科学基金(62501333);鹏城实验室重大攻关项目(PCL2024A01;PCL2025A03);移动信息网络国家科技重大专项(2024ZD1300700);贵州省基础研究计划（No.Qiankehejichu-MS[2026]269）、陕西省自然科学基础研究计划(2025JC-YBQN-924)
- DOI：
  中图分类号：
- 收稿：2025-10-22，
  
  修回：2026-02-25，
  
  录用：2026-02-28，
- 稿件说明：
移动端阅览
张中强, 邱奇, 苗雅杰, 等. 面向无人机航拍图像传输的信道弹性编解码方法[J/OL]. 物联网学报, 2026.

张中强, 邱奇, 苗雅杰, et al. A channel elastic encoding and decoding method for unmanned aerial vehicle image transmission[J/OL]. Chinese Journal on Internet of Things, 2026.
张中强, 邱奇, 苗雅杰, 等. 面向无人机航拍图像传输的信道弹性编解码方法[J/OL]. 物联网学报, 2026. DOI：

张中强, 邱奇, 苗雅杰, et al. A channel elastic encoding and decoding method for unmanned aerial vehicle image transmission[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

无人机（UAVs

unmanned aerial vehicles）是低空经济网络的核心组成部分，实现无人机航拍图像高效传输在空地通信中至关重要。在无人机执行任务过程中，由于飞行区域的变化，导致信道状态快速波动，如信噪比（SNR

signal-to-noise ratio）和传输速率。为适应这些变化，提出一种面向无人机航拍图像传输的信道弹性编解码方法。该方法包含：轻量化特征提取模块、特征增强模块、传输速率自适应模块，和相应的解码模块。轻量化特征提取模块利用残差块的局部细节提取能力与轻量Mamba的全局上下文建模能力，有效获取航拍图像丰富的语义特征。特征增强模块根据信道状态SNR，增强关键语义特征。传输速率自适应模块根据信道传输速率，自适应调整传输特征的数量。在航拍图像数据集（AID

aerial image dataset）上的实验结果表明，该方法比现有方法具有更高的传输精度。

Abstract

Unmanned Aerial Vehicles (UAVs) are recognized as a core component of the low-altitude economy network. It is crucial to achieve efficient image transmission in air-to-ground communication. During mission execution

rapid channel fluctuations are induced by changes in the flight area

including variations in signal-to-noise ratio (SNR) and transmission rate. To adapt to these changes

a channel elastic encoding and decoding method for UAV image transmission was proposed. This method included: a lightweight feature extraction module

a channel feature enhancement module

a transmission rate adaptive module

and the symmetric decoding modules. The lightweight feature extraction module contained residual blocks and the mobile Mamba

which could quickly extract local detail features and global context. It could effectively obtain rich semantic features from aerial image. The feature enhancement module enhanced key semantic features according to the channel state SNR. The transmission rate adaptive module adjusted the size of transmitted features based on the channel transmission rate. Extensive experiments on the AID datasets demonstrated that the proposed method achieved higher transmission accuracy than state-of-the-art methods.

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