论文标题
基于空间的全球海事监视。第二部分：人工智能和数据融合技术
Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques
论文作者
论文摘要
海上监视（MS）对于搜救行动，渔业监测，污染控制，执法，移民监测和国家安全政策至关重要。由于基于地面的雷达和自动识别系统（AIS）并不总是对整个海事域提供全面且无缝的覆盖范围，因此使用太空传感器对于补充它们至关重要。我们在这项工作的第一部分中回顾了MS基于空间的技术，标题为“基于空间的全球海上监视。第一部分：卫星技术” [1]。但是，将多个地面和空间传感器与其他信息源相结合的未来MS系统将需要专门的人工智能和数据融合技术来处理原始卫星图像和融合异构信息。我们工作的第二部分重点是使用基于空间的传感器为MS的最有希望的人工智能和数据融合技术。
Maritime surveillance (MS) is of paramount importance for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since ground-based radars and automatic identification system (AIS) do not always provide a comprehensive and seamless coverage of the entire maritime domain, the use of space-based sensors is crucial to complement them. We reviewed space-based technologies for MS in the first part of this work, titled "Space-based Global Maritime Surveillance. Part I: Satellite Technologies" [1]. However, future MS systems combining multiple terrestrial and space-based sensors with additional information sources will require dedicated artificial intelligence and data fusion techniques for the processing of raw satellite images and fuse heterogeneous information. The second part of our work focuses on the most promising artificial intelligence and data fusion techniques for MS using space-based sensors.
