发表机构
Indiana University Bloomington; US Air Force Research Lab (AFRL)（印第安纳大学伯明顿分校; 美国空军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对遥感目标检测标注成本高及现有主动学习方法的挑战，提出基础模型协作主动学习和半自动标注框架，构建双源机制，采用多种策略提升样本选择质量与覆盖率，减轻人工负担，实验证明该方法在多数据集上表现优异。
AI 中文摘要
遥感目标检测的标注成本很高，而现有的主动学习方法在目标检测场景中仍面临一些挑战，包括定位和分类不确定性的耦合、冷启动阶段严重的定位噪声以及高召回率候选提议导致的伪多样性。为了解决这些问题，我们提出了一个基础模型协作主动学习和半自动标注框架，用于高效构建遥感目标检测数据集。我们构建了一个双源机制，由基于UPN+SAM2的参考定位源（SA源）和检测器预测源（OD源）组成，并进一步提出了基础模型增强的双源不确定性估计，通过联合建模定位一致性和分类置信度来提高冷启动阶段的样本选择质量。此外，我们提出了以目标为中心的多样性采样，使用DINOv2特征和SAM2掩码构建目标级表示，以提高样本覆盖率，同时抑制伪多样性。为了解决半自动标注阶段的几何噪声问题，我们设计了双源框切换，用来自SA源的匹配细化框替换有噪声的检测器框，从而减轻框细化的人工负担。在DIOR、HRSC2016、DOTAv2和FAIR1M上的实验表明，我们的方法在大多数标注预算下都取得了优异或可比的结果，在低预算情况下冷启动样本效率明显更高。
英文摘要
The annotation cost for remote sensing object detection is high, while existing active learning methods still face several challenges in object detection scenarios, including the coupling of localization and classification uncertainty, severe localization noise in the cold-start stage, and pseudo-diversity caused by high-recall candidate proposals. To address these issues, we propose a foundation-model-collaborative active learning and semi-automatic annotation framework for efficient construction of remote sensing object detection datasets. We build a dual-source mechanism consisting of a reference localization source (SA-source) based on UPN+SAM2 and a detector prediction source (OD-source), and further propose a Foundation-model-enhanced Dual-Source Uncertainty estimation to improve sample selection quality in the cold-start stage by jointly modeling localization consistency and classification confidence. Furthermore, we propose Object-Centric Diversity Sampling, which constructs object-level representations using DINOv2 features and SAM2 masks to improve sample coverage while suppressing pseudo-diversity. To address geometric noise in the semi-automatic annotation stage, we design Dual-Source Box Switching, which replaces noisy detector boxes with matched refined boxes from the SA-source, thereby reducing the manual burden of box refinement. Experiments on DIOR, HRSC2016, DOTAv2, and FAIR1M show that our method achieves superior or comparable results under most annotation budgets, with notably stronger cold-start sample efficiency in the low-budget regime.
