标题： 面向机载卫星地球观测图像分类的语义知识蒸馏 显示英文标题

标题： Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image Classification

摘要： 本研究提出了一种创新的动态加权知识蒸馏（KD）框架，专门针对资源受限环境下的高效地球观测（EO）图像分类（IC）。 利用EfficientViT和MobileViT作为教师模型，该框架使轻量级学生模型，特别是ResNet8和ResNet16，能够达到90%以上的准确率、精确率和召回率，符合可靠分类任务所需的严格置信度阈值。 与依赖静态权重分配的传统KD方法不同，我们的自适应加权机制会根据每个教师模型的置信度进行响应，使学生模型能够动态地优先考虑更可信的知识来源。 值得注意的是，ResNet8在效率方面表现出显著提升，相比MobileViT，参数减少了97.5%，FLOPs减少了96.7%，功耗降低了86.2%，推理速度提高了63.5%。 这种对复杂性和资源需求的重大优化，使ResNet8成为EO任务的理想候选模型，结合了强大的性能和部署可行性。 基于置信度的可适应KD方法突显了动态蒸馏策略在生成高性能、资源高效的模型方面的潜力，这些模型专为基于卫星的EO应用而设计。 可重复使用的代码可在我们的GitHub仓库中获取。 显示英文摘要

摘要： This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constrained settings. Utilizing EfficientViT and MobileViT as teacher models, this framework enables lightweight student models, particularly ResNet8 and ResNet16, to surpass 90% in accuracy, precision, and recall, adhering to the stringent confidence thresholds necessary for reliable classification tasks. Unlike conventional KD methods that rely on static weight distribution, our adaptive weighting mechanism responds to each teacher model's confidence, allowing student models to prioritize more credible sources of knowledge dynamically. Remarkably, ResNet8 delivers substantial efficiency gains, achieving a 97.5% reduction in parameters, a 96.7% decrease in FLOPs, an 86.2% cut in power consumption, and a 63.5% increase in inference speed over MobileViT. This significant optimization of complexity and resource demands establishes ResNet8 as an optimal candidate for EO tasks, combining robust performance with feasibility in deployment. The confidence-based, adaptable KD approach underscores the potential of dynamic distillation strategies to yield high-performing, resource-efficient models tailored for satellite-based EO applications. The reproducible code is accessible on our GitHub repository.