The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS). As the scale of RS data continues to expand, annotation procedures increasingly rely on thematic products or crowdsourced procedures to reduce the cost of manual annotation. While cost-effective, these strategies often introduce multi-label noise in the form of partially incorrect annotations. In MLC, label noise arises as additive noise, subtractive noise, or a combination of both in the form of mixed noise. Previous work has largely overlooked this distinction and commonly treats noisy annotations as supervised signals, lacking mechanisms that explicitly adapt learning behavior to different noise types. To address this limitation, we propose NAR, a noise-adaptive regularization method that explicitly distinguishes between additive and subtractive noise within a semi-supervised learning framework. NAR employs a confidence-based label handling mechanism that dynamically retains label entries with high confidence, temporarily deactivates entries with moderate confidence, and corrects low confidence entries via flipping. This selective attenuation of supervision is integrated with early-learning regularization (ELR) to stabilize training and mitigate overfitting to corrupted labels. Experiments across additive, subtractive, and mixed noise scenarios demonstrate that NAR consistently improves robustness compared with existing methods. Performance improvements are most pronounced under subtractive and mixed noise, indicating that adaptive suppression and selective correction of noisy supervision provide an effective strategy for noise robust learning in RS MLC.

翻译：多标签分类（MLC）方法的可靠性开发已成为遥感（RS）领域的重要研究方向。随着遥感数据规模的持续扩大，标注过程日益依赖专题产品或众包流程以降低人工标注成本。尽管这些策略具有成本效益，但常以部分错误标注的形式引入多标签噪声。在多标签分类中，标签噪声表现为加性噪声、减性噪声或两者混合的复合噪声形式。先前研究大多忽视这种区别，通常将噪声标注视为监督信号，缺乏针对不同噪声类型显式调整学习行为的机制。为克服这一局限，我们提出NAR——一种在半监督学习框架内显式区分加性与减性噪声的噪声自适应正则化方法。NAR采用基于置信度的标签处理机制：动态保留高置信度标签项，暂时停用中等置信度项，并通过翻转校正低置信度项。这种选择性监督衰减与早期学习正则化（ELR）相结合，以稳定训练并缓解对损坏标签的过拟合。在加性、减性及混合噪声场景下的实验表明，相较于现有方法，NAR能持续提升鲁棒性。在减性与混合噪声条件下性能提升尤为显著，这表明自适应抑制与选择性校正噪声监督为遥感多标签分类中的噪声鲁棒学习提供了有效策略。