用卷积自编码器实现小卫星数据压缩与异常检测。
Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies
- 设计卷积自编码器，兼顾数据压缩与原位异常检测。
- 在非洲灾情监测图像上验证，支持高效传输与智能采样。
- 适合小卫星平台，推动非洲地区航天与AI融合应用。
小卫星技术通过简化设计和降低成本，提升了地球探测任务的可行性与频率。星上数据采集系统可借助机器学习提升图像处理与特征提取效率。本文提出一种适用于小卫星载荷的卷积自编码器，兼具数据压缩与原位异常检测双重功能，以优化下行传输并指导数据采集。该方法在非洲大陆航空图像灾情监测场景中得到验证，为小卫星领域的新型基于机器学习的解决方案提供了可能，并助力非洲地区航天技术与人工智能的协同发展。
原文摘要 · Abstract (English)
Small satellite technologies have enhanced the potential and feasibility of geodesic missions, through simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition systems can benefit from the implementation of machine learning (ML), for better performance and greater efficiency on tasks such as image processing or feature extraction. This work presents convolutional autoencoders for implementation on the payload of small satellites, designed to achieve dual functionality of data compression for more efficient off-satellite transmission, and at-source anomaly detection to inform satellite data-taking. This capability is demonstrated for a use case of disaster monitoring using aerial image datasets of the African continent, offering avenues for both novel ML-based approaches in small satellite applications along with the expansion of space technology and artificial intelligence in Africa.
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