发表机构
Washington State University; German Aerospace Center (DLR)（华盛顿州立大学; 德国航空航天中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SatLabel框架，通过自适应样本采集与半监督学习，以极低参数和能耗实现星载卫星图像高效标注，优于RemoteCLIP。
AI 中文摘要
大规模普适感知日益依赖高分辨率卫星图像，然而特定任务的星载视觉受限于昂贵的标注以及有限的计算、内存、能量和通信资源。现有方法主要依赖数据饥渴的监督学习或大型视觉-语言基础模型，在这些约束下限制了高效适应和部署。我们提出了SatLabel，一种资源感知的学习框架，通过自适应样本采集和半监督模型适应，将有限的卫星标签转化为逐步精炼的星载模型。不同于在均匀采样的标签上重复训练，SatLabel在模型不确定性、类别不平衡和伪标签质量之间闭环，选择性地获取信息丰富的样本，同时利用丰富的未标记图像。这使得紧凑的学生模型能够以更少的标注和推理成本适应目标感知领域。我们进一步引入了一种可选的基于图的特征精炼的专家混合（MoE）学生模型，以增强表示能力同时保持轻量级足迹。我们在11个遥感数据集上评估了SatLabel，涵盖核心、扩展和未见领域，并与RemoteCLIP零样本推理进行比较。SatLabel在大多数核心和扩展数据集上提高了Macro-F1，同时保持对未见领域的强跨数据集迁移能力。更重要的是，Balanced学生模型仅包含1120万参数，占用约42.8 MB，而RemoteCLIP为1.513亿参数和577 MB，同时需要3.65 GFLOPs对比5.89 GFLOPs。在四个效率基准上，它实现了约2倍高的GPU前向吞吐量，并降低了数据集上的每图像能量消耗。
英文摘要
Large-scale pervasive sensing increasingly relies on high-resolution satellite imagery, yet task-specific onboard vision is constrained by costly annotation and limited computation, memory, energy, and communication resources. Existing approaches largely rely on either data-hungry supervised learning or large vision-language foundation models, limiting efficient adaptation and deployment under these constraints. We present SatLabel, a resource-aware learning framework that transforms limited satellite labels into progressively refined onboard models through adaptive sample acquisition and semi-supervised model adaptation. Rather than repeatedly training on uniformly sampled labels, SatLabel closes the loop between model uncertainty, class imbalance, and pseudo-label quality to selectively acquire informative samples while exploiting abundant unlabeled imagery. This enables a compact student to adapt to target sensing domains with reduced annotation and inference costs. We further introduce an optional Mixture-of-Experts (MoE) student with graph-based feature refinement to enhance representation capacity while retaining a lightweight footprint. We evaluate SatLabel on 11 remote-sensing datasets spanning core, extended, and unseen domains against RemoteCLIP zero-shot inference. SatLabel improves Macro-F1 on most core and extended datasets while maintaining strong cross-dataset transfer to unseen domains. More importantly, the Balanced student contains only 11.2 M parameters and occupies approximately 42.8 MB, compared with 151.3M parameters and 577 MB for RemoteCLIP, while requiring 3.65 versus 5.89 GFLOPs. Across four efficiency benchmarks, it achieves approximately 2x higher GPU-forward throughput and reduces energy per image on datasets.
