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This paper introduces a self-supervised neural network encoder and anomaly detection model for onboard marine environmental hazard detection on Earth observation satellites. The pipeline compresses multi/hyperspectral imagery into a latent space for efficient anomaly identification, outperforming traditional methods like Isolation Forest and OC-SVM. This approach enables timely alerts and reduced data transmission for satellites with limited computational resources, as demonstrated by its integration into ESA's Phisat-2 and IMAGIN-e missions.
A single AI pipeline can now detect marine environmental anomalies directly on satellites, drastically reducing data transmission needs and enabling real-time alerts.
Marine ecosystems are impacted by various threats such as oil spills, algal blooms, and sediment floods, which disrupt habitats, wildlife, and human activities. Advances in satellite imagery and Artificial Intelligence (AI) have enhanced our capabilities for early detection and mitigation of such hazards. In this paper, we propose a marine event detection pipeline for Earth observation satellites equipped with multi- or hyperspectral sensors. Our approach includes a self-supervised neural network encoder that compresses satellite images into a reduced latent space, enabling efficient onboard processing. A machine learning anomaly detection model identifies deviations from normal sea patterns to detect environmental anomalies. We compare its performance against traditional algorithms such as Isolation Forest, One-Class Support Vector Machine and Local Outlier Factors. Our lightweight, resource-efficient pipeline is optimized for deployment on satellites with limited computational resources, ranging from embedded CPUs to AI hardware accelerators. By prioritizing the transmission of critical information, our solution enhances system responsiveness and optimizes satellite communication bandwidth. Demonstrated through current integration across multiple missions, including European Space Agency's (ESA) Phisat-2 mission and Microsoft/Thales Alenia Space IMAGIN-e mission, our pipeline aims to improve marine environmental monitoring by providing timely alerts and efficient data reduction.