On-Board Anomaly Detection for Efficient Marine Environmental Monitoring

Oil spills, algal blooms, and sediment floods harm marine ecosystems, and satellites could flag them early, but downlink bandwidth and onboard compute are limited. The authors propose a pipeline for multiand hyperspectral satellites: a self-supervised encoder compresses imagery into a compact latent space, and an anomaly detector flags deviations from normal sea patterns, compared against Isolation Forest, One-Class SVM, and Local Outlier Factor. Designed for embedded CPUs and AI accelerators, it prioritizes transmitting critical information. It is already integrated in missions including ESA's Phisat-2.

Authors: Thomas Goudemant, Clotilde Szywala, Benjamin Francesconi, Michelle Aubrun, Yves Bobichon, Marjorie Bellizzi, Adrien Girard

Paper: https://arxiv.org/abs/2610.03649v1