Cognitive Semantic Augmentation LEO Satellite Networks for Earth Observation

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.