Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques. The proposed system leverages Discrete Task-Oriented Joint Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) integrate cognitive semantic processing with inter-satellite links, enabling efficient analysis and transmission of multispectral imagery for improved object detection, pattern recognition, and real-time decision-making. Cognitive Semantic Augmentation (CSA) is introduced to enhance a system's capability to process and transmit semantic information, improving feature prioritization, consistency, and adaptation to changing communication and application needs. The end-to-end architecture is designed for next-generation satellite networks, such as those supporting 6G, demonstrating significant improvements in fewer communication rounds and better accuracy over federated learning.

翻译：地球观测系统对于测绘、灾害监测和资源管理至关重要，但其在处理和高效传输海量地球观测数据方面存在困难，特别是在农业和实时灾害响应等专业应用中。本文提出了一种用于地球观测卫星网络的语义通信新框架，旨在通过认知处理技术提升数据传输效率和系统性能。所提出的系统利用离散任务导向的联合信源信道编码与语义数据增强技术，将认知语义处理与星间链路相结合，从而实现对多光谱图像的高效分析与传输，以改进目标检测、模式识别和实时决策能力。本文引入了认知语义增强技术，以增强系统处理和传输语义信息的能力，改善特征优先级排序、一致性以及对不断变化的通信和应用需求的适应性。该端到端架构专为支持6G等下一代卫星网络设计，在联邦学习框架下展现出通信轮次显著减少和精度显著提升的优势。