Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI) bring exciting opportunities to various fields of science and industry that can directly benefit from in-orbit data processing. Taking AI into space may accelerate the response to various events, as massively large raw hyperspectral images (HSIs) can be turned into useful information onboard a satellite; hence, the images’ transfer to the ground becomes much faster and offers enormous scalability of AI solutions to areas across the globe. However, there are numerous challenges related to hardware and energy constraints, resource frugality of (deep) machine learning models, availability of ground truth data, and building trust in AI-based solutions. Unbiased, objective, and interpretable selection of an AI application is of paramount importance for emerging missions, as it influences all aspects of satellite design and operation. In this article, we tackle this issue and introduce a quantifiable procedure for objectively assessing potential AI applications considered for onboard deployment. To prove the flexibility of the suggested technique, we utilize the approach to evaluate AI applications for two fundamentally different missions: the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) [European Union/European Space Agency (ESA)] and the 6U nanosatellite Intuition-1 (KP Labs). We believe that our standardized process may become an important tool for maximizing the outcome of Earth observation (EO) missions through selecting the most relevant onboard AI applications in terms of scientific and industrial outcomes.

中文翻译：

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客观筛选高光谱对地观测应用，将人工智能带入太空：让卫星任务的“大脑”靠近“眼睛”

遥感高光谱成像和人工智能（AI）的最新进展为科学和工业的各个领域带来了令人兴奋的机遇，可以直接受益于在轨数据处理。将人工智能带入太空可能会加速对各种事件的响应，因为大量原始高光谱图像（HSI）可以在卫星上转化为有用的信息；因此，图像传输到地面的速度变得更快，并为全球各地的人工智能解决方案提供了巨大的可扩展性。然而，存在许多与硬件和能源限制、（深度）机器学习模型的资源节约、地面实况数据的可用性以及对基于人工智能的解决方案建立信任相关的挑战。公正、客观、人工智能应用程序的可解释选择对于新兴任务至关重要，因为它影响卫星设计和操作的各个方面。在本文中，我们解决了这个问题，并介绍了一种可量化的程序，用于客观评估考虑用于机载部署的潜在人工智能应用程序。为了证明所建议技术的灵活性，我们利用该方法来评估两项根本不同的任务的人工智能应用：哥白尼环境高光谱成像任务（CHIME）[欧盟/欧洲航天局（ESA）]和6U纳米卫星直觉-1（KP 实验室）。