Problem
Research questions and friction points this paper is trying to address.
task-oriented
semantic feature transmission
low-SNR channels
satellite remote sensing
Task-Oriented Semantic Feature Transmission for Multi-Task Satellite Remote Sensing over Low-SNR Channels
🤖 AI Summary
本文针对低信噪比条件下卫星遥感多任务处理问题，提出了一种直接传输语义特征的任务导向框架，通过轻量级通道适应模块和特征恢复器优化任务性能。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Task-Oriented Framework
Semantic Feature Transmission
Channel Adaptation Module (CAM)
Feature Restorer
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