有效载荷(计算)
计算机科学
正方体卫星
多光谱图像
数据处理
图像处理
云计算
实时计算
人工智能
计算机硬件
嵌入式系统
卫星
数据库
操作系统
工程类
航空航天工程
图像(数学)
计算机网络
网络数据包
作者
Nicola Melega,Nicolas Longépé,V. Marchese,Agne Paskeviciute,Oriol Aragon,Irina B. Babkina,Alessandro Marin,Jakub Nalepa,Léonie Buckley,Giorgia Guerrisi,Sofia Oliviera,Heike Stein
摘要
The Φsat-2 mission from the European Space Agency (ESA) is part of Φsat mission lineup aimed to address innovative mission concepts making use of advanced onboard processing including Artificial Intelligence. Φsat-2 is based on a 6U CubeSat with a medium-high resolution VIS/NIR multispectral payload (eight bands plus NIR) combined with a hardware accelerated unit capable of running several AI applications throughout the mission lifetime. As images are acquired, and after the application of dTDI processing, the raw data is transferred through SpaceWire to a payload pre-processor where level L1B will be produced. At this stage radiometric and geometric processing are carried out in conjunction with georeferencing. Once the data is pre-processed, it is fed to the AI processor through the primary computer and made available to the onboard applications; orchestration is done via a dedicated version of the NanoSat MO Framework. The following applications are currently baselined and additional two will be selected via dedicated AI Challenge by Q3 2023: SAT2MAP for autonomous detection of streets during emergency scenarios; Cloud Detection application and service for data reduction; the Autonomous Vessel Awareness to detect and classify vessel types and the deep compression application (CAE) that has the goal of reducing the amount of acquired data to improve the mission effectiveness.
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