On-Board Anomaly Detection for Efficient Marine Environmental Monitoring
[Widzenie komputerowe] Autorzy: Thomas Goudemant, Clotilde Szywala, Benjamin Francesconi i 4 więcej
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, re…
- Kategoria arxiv
- cs.CV
- Kategoria
- Widzenie komputerowe
- Doi
- 10.5281/zenodo.13850918