发表机构
Beijing University of Posts and Telecommunications（北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种任务导向的语义特征传输框架，绕过图像重建直接传输多任务预训练特征，在低信噪比信道下显著提升分类与检测性能。
AI 中文摘要
传统的卫星遥感传输遵循先重建后推理的范式，优化像素级保真度，这导致与下游任务（如分类和检测）的目标不匹配，尤其是在低信噪比条件下。本文研究了一种任务导向的框架，该框架绕过图像重建，直接传输由多任务预训练骨干网络提取的语义特征。一个轻量级信道适配模块（CAM）压缩特征维度以减少带宽，而特征恢复器在信道损坏后恢复任务相关结构。在骨干网络冻结的情况下，CAM和任务特定的下游头部在随机信噪比训练下，通过任务级和特征级监督进行联合优化。在所采用的AWGN设置下，场景分类和目标检测的实验表明，在不同信噪比条件下，相较于面向重建的JSCC基线，该方法均有一致的性能提升，其中在低信噪比区域改进最大。
英文摘要
Conventional satellite remote sensing transmission follows a reconstruct-then-infer paradigm that optimizes pixel-level fidelity, creating an objective mismatch with downstream tasks such as classification and detection, especially at low SNR. This paper investigates a task-oriented framework that bypasses image reconstruction and directly transmits semantic features extracted by a multitask-pretrained backbone. A lightweight channel adaptation module (CAM) compresses feature dimensionality for bandwidth reduction, and a feature restorer recovers task-relevant structure after channel corruption. With the backbone frozen, the CAM and task-specific downstream heads are jointly optimized with task and feature-level supervision under random-SNR training. Under the adopted AWGN setting, experiments on scene classification and object detection show consistent gains over reconstruction-oriented JSCC baselines across different SNR conditions, with the largest improvements in the low-SNR regime.
