arXiv cs.AI· Clotilde Szywala, Thomas Goudemant, Marjorie Bellizzi, Benjamin Francesconi, Adrien Girard·· 9 天前AI 评分24
Φsat-2 星载海洋异常检测：从仿真开发到在轨验证
Onboard Marine Anomaly Detection on $\Phi$sat-2: From Simulation-Based Development to In-Orbit Demonstration
AI 导读
欧洲航天局 Φsat-2 任务部署了一套轻量级星载海洋异常检测流水线，结合海面分割、自监督特征编码与基于偏离正常海况的通用异常检测。该流水线发射前基于仿真 Φsat-2 影像训练验证，在轨后早期实验发现仿真与真实数据存在显著不匹配，遂改用真实 Level-1 影像和更优标注策略重新训练，性能大幅提升。该经验表明仿真开发有助于降低飞行前风险，但可靠的科学验证仍需有代表性的在轨数据。
正文 · 原文
Abstract:Onboard Artificial Intelligence can improve responsiveness and bandwidth efficiency of Earth Observation systems by processing data directly on the satellite. This paper presents the experience gained from the development, onboard integration, and post-launch adaptation of a lightweight marine anomaly detection pipeline deployed on the European Space Agency's $\Phi$sat-2 mission. The application combines sea segmentation, self-supervised feature encoding of marine regions, generic anomaly detection based on deviations from a normal sea state, and optional characterization of selected anomaly types. Before launch, the pipeline was trained and validated on simulated $\Phi$sat-2 imagery to assess algorithmic performance and compatibility with resource-constrained onboard hardware. After integration and functional validation in the mission environment, early experiments on real $\Phi$sat-2 acquisitions revealed a significant mismatch between simulated and in-orbit data. The pipeline was therefore retrained on real Level-1 imagery using an improved annotation strategy to better handle ambiguous marine regions, substantially enhancing performance. Beyond demonstrating the onboard feasibility of the application, the $\Phi$sat-2 experience highlights the importance of robust annotation strategies and sensor-aware design, and shows that simulation-based development is valuable for pre-flight risk reduction, while reliable scientific validation requires representative in-orbit data and should be clearly distinguished from functional validation.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.11735 [cs.AI] |
| (or arXiv:2610.11735v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11735 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Thomas Goudemant [view email] [via CCSD proxy]
[v1]
Thu, 8 Oct 2026 11:31:06 UTC (784 KB)
来源：arXiv cs.AI · arxiv.org