Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constellation for scene classification using semi-supervised learning while accounting for operational constraints such as temperature and limited power budgets based on satellite processor benchmarks of the neural network. We evaluate mission scenarios employing both decentralised and federated learning approaches. All scenarios achieve convergence to high accuracy (around 91% on EuroSAT RGB dataset) within a one-day mission timeframe.

翻译：摘要：在最新的卫星硬件上运行星上机器学习有望显著节省通信与运营成本。我们展示了在卫星星座上利用半监督学习进行场景分类的机器学习模型训练过程，同时基于卫星处理器神经网络基准测试，考虑了温度与有限功率预算等运行约束。我们评估了采用分散式与联邦式学习方法的任务场景。所有场景均能在一天的任务时间窗口内收敛到高精度（在EuroSAT RGB数据集上约91%）。