The development of supervised deep learning-based methods for multi-label scene classification (MLC) is one of the prominent research directions in remote sensing (RS). Yet, collecting annotations for large RS image archives is time-consuming and costly. To address this issue, several data augmentation methods have been introduced in RS. Among others, the data augmentation technique CutMix, which combines parts of two existing training images to generate an augmented image, stands out as a particularly effective approach. However, the direct application of CutMix in RS MLC can lead to the erasure or addition of class labels (i.e., label noise) in the augmented (i.e., combined) training image. To address this problem, we introduce a label propagation (LP) strategy that allows the effective application of CutMix in the context of MLC problems in RS without being affected by label noise. To this end, our proposed LP strategy exploits pixel-level class positional information to update the multi-label of the augmented training image. We propose to access such class positional information from reference maps associated to each training image (e.g., thematic products) or from class explanation masks provided by an explanation method if no reference maps are available. Similarly to pairing two training images, our LP strategy carries out a pairing operation on the associated pixel-level class positional information to derive the updated multi-label for the augmented image. Experimental results show the effectiveness of our LP strategy in general and its robustness in the case of various simulated and real scenarios with noisy class positional information in particular.

翻译：基于监督深度学习的多标签场景分类方法的发展是遥感领域的重要研究方向之一。然而，为大规模遥感图像档案收集标注既耗时又昂贵。为解决这一问题，遥感领域已引入了多种数据增强方法。其中，CutMix数据增强技术通过组合两幅现有训练图像的部分区域来生成增强图像，成为一种尤为有效的方法。然而，在遥感多标签分类中直接应用CutMix可能导致增强后的训练图像出现类别标签的缺失或误增（即标签噪声）。为解决此问题，我们提出了一种标签传播策略，使得CutMix能够有效应用于遥感多标签分类问题而不受标签噪声影响。为此，我们提出的标签传播策略利用像素级类别位置信息来更新增强训练图像的多标签。我们建议通过两种方式获取此类位置信息：当存在参考图时（如专题产品），从每幅训练图像关联的参考图中提取；若无参考图，则通过解释方法提供的类别解释掩码获取。与配对两幅训练图像的操作类似，我们的标签传播策略对关联的像素级类别位置信息执行配对操作，从而推导出增强图像的更新多标签。实验结果表明，我们的标签传播策略整体上具有显著效果，尤其在面对带有噪声类别位置信息的各种模拟和真实场景时表现出良好的鲁棒性。