Scalable Data Transmission Framework for Earth Observation Satellites with Channel Adaptation

TL;DR

This paper introduces a scalable, adaptive data transmission framework for Earth observation satellites that prioritizes critical information and dynamically adjusts to channel conditions, significantly reducing data transmission while maintaining quality.

Contribution

It proposes a novel semantic communication-based framework that enhances data transmission efficiency for EO satellites through pixel importance assessment and channel-aware adaptation.

Findings

Reduces data transmission volume significantly

Maintains high-quality data delivery under varying channel conditions

Proven effective on real and simulated datasets

Abstract

The immense volume of data generated by Earth observation (EO) satellites presents significant challenges in transmitting it to Earth over rate-limited satellite-to-ground communication links. This paper presents an efficient downlink framework for multi-spectral satellite images, leveraging adaptive transmission techniques based on pixel importance and link capacity. By integrating semantic communication principles, the framework prioritizes critical information, such as changed multi-spectral pixels, to optimize data transmission. The process involves preprocessing, assessing pixel importance to encode only significant changes, and dynamically adjusting transmissions to match channel conditions. Experimental results on the real dataset and simulated link demonstrate that the proposed approach ensures high-quality data delivery while significantly reducing number of transmitted data, making it highly suitable for satellite-based EO applications.