On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites

TL;DR

This paper introduces an energy-efficient change detection method for Earth observation satellites that combines cloud removal and change encoding to optimize data transmission.

Contribution

It proposes a novel joint design of cloud filtering and change encoding using deep learning models for resource-efficient satellite data downlink.

Findings

Significant reduction in energy consumption during data transmission.

High accuracy in detecting and encoding changed multi-spectral pixels.

Effective preservation of important information in satellite images.

Abstract

The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consists of the joint design of cloud removal and change encoding, which can be seen as an instance of semantic communication, as it encodes important information, such as changed multi-spectral pixels (MPs), while aiming to minimize energy consumption. It comprises a three-stage end-to-end scoring mechanism that determines the importance of each MP before deciding its transmission. Specifically, the sensing image is (1) standardized, (2) passed through a high-performance cloud filtering via the Cloud-Net model, and (3) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them and forward the result to the ground station. The experimental results indicate that the proposed framework is effective in optimizing energy usage while preserving high-quality data transmission in satellite-based Earth observation applications.