发表机构
Institut de Recherche Technologique Saint Exupéry（圣埃克苏佩里技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种面向多光谱/高光谱卫星的轻量级海洋事件检测流程，利用自监督编码器和异常检测模型，在资源受限的星载硬件上实现高效环境监测与及时警报。
AI 中文摘要
海洋生态系统受到石油泄漏、藻华和沉积物洪水等各种威胁的影响，这些威胁破坏了栖息地、野生动物和人类活动。卫星成像和人工智能（AI）的进步增强了我们早期检测和缓解此类危害的能力。在本文中，我们提出了一种针对配备多光谱或高光谱传感器的对地观测卫星的海洋事件检测流程。我们的方法包括一个自监督神经网络编码器，将卫星图像压缩到降维的潜在空间中，从而实现高效的星载处理。一种机器学习异常检测模型识别与正常海洋模式的偏差，以检测环境异常。我们将其性能与隔离森林、一类支持向量机和局部异常因子等传统算法进行了比较。我们轻量级、资源高效的流程针对在计算资源有限的卫星上部署进行了优化，范围从嵌入式CPU到AI硬件加速器。通过优先传输关键信息，我们的解决方案增强了系统响应能力并优化了卫星通信带宽。通过当前在多个任务中的集成展示，包括欧洲空间局（ESA）的Phisat-2任务和微软/泰雷兹阿莱尼亚宇航公司的IMAGIN-e任务，我们的流程旨在通过提供及时警报和高效数据缩减来改善海洋环境监测。
英文摘要
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.
Comments8 pages, 3 figures. Presented at the 9th International Workshop on On-Board Payload Data Compression (OBPDC 2024), Gran Canaria, Spain, 2-4 October 2024
Journal refProceedings of the 9th International Workshop on On-Board Payload Data Compression (OBPDC 2024), Gran Canaria, Spain, 2-4 October 2024
