低地球轨道场景分类的分散式半监督星上学习
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
AI总结：
针对低轨卫星场景分类，提出分散式半监督星上学习方法，考虑运行约束，在一天内达到约91%精度。
摘要：
最新卫星硬件上的星上机器学习为显著节省通信和运营成本提供了潜力。我们展示了在卫星星座上利用半监督学习训练机器学习模型进行场景分类的过程，同时基于卫星处理器对神经网络的基准测试，考虑了温度、有限功率预算等运行约束。我们评估了采用分散式和联邦式学习方法的任务场景。所有场景均在一个任务日内达到高精度收敛（在EuroSAT RGB数据集上约为91%）。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
