On-Board Anomaly Detection for Efficient Marine Environmental Monitoring

The paper presents a marine event detection pipeline for Earth observation satellites that uses a self‑supervised neural network encoder to compress images into a latent space, followed by a machine learning anomaly detection model to spot deviations from normal sea patterns. It compares this approach with traditional algorithms such as Isolation Forest, One‑Class Support Vector Machine, and Local Outlier Factors, demonstrating superior performance. The lightweight, resource‑efficient design is tailored for satellites with limited computational resources and has been integrated into missions like ESA’s Phisat‑2 and Microsoft/Thales Alenia Space IMAGIN‑e, enabling timely alerts and efficient data reduction.

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