arXiv：cs.LG· Thomas Goudemant, Clotilde Szywala, Benjamin Francesconi, Michelle Aubrun, Yves Bobichon, Marjorie Bellizzi, Adrien Girard·· 5 小时前AI 评分31
面向高效海洋环境监测的星上异常检测流水线
On-Board Anomaly Detection for Efficient Marine Environmental Monitoring
AI 导读
研究者提出一套面向多光谱/高光谱对地观测卫星的海洋事件检测流水线，用自监督神经网络编码器将卫星图像压缩到低维潜在空间，再由机器学习异常检测模型识别偏离正常海面模式的异常。
正文
Abstract: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.
| Comments: | 8 pages, 3 figures. Presented at the 9th International Workshop on On-Board Payload Data Compression (OBPDC 2024), Gran Canaria, Spain, 2-4 October 2024 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; I.4.6; I.5.2 |
| Cite as: | arXiv:2610.03649 [cs.CV] |
| (or arXiv:2610.03649v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03649 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Proceedings of the 9th International Workshop on On-Board Payload Data Compression (OBPDC 2024), Gran Canaria, Spain, 2-4 October 2024 |
| Related DOI: | https://doi.org/10.5281/zenodo.13850918
DOI(s) linking to related resources |
Submission history
From: Thomas Goudemant [view email]
[v1]
Fri, 2 Oct 2026 17:32:34 UTC (710 KB)
来源：arXiv：cs.LG · arxiv.org