The joint classification of multispectral (MS) and panchromatic (PAN) images aims to provide a more detailed and accurate interpretation of land features. Although deep-learning-based methods have achieved remarkable success in this task, the generalization performance of networks is compromised when labeled samples are insufficient. In this study, we explore the possibility of leveraging unlabeled remote sensing images (RSIs) through contrastive learning and demonstrate the challenges associated with directly applying contrastive learning to RSIs. To end this, we propose a cross-source contrastive learning method for few-shot MS and PAN joint classification (CrossCLMP), which aims to learn sufficient transferable representations in a self-supervised contrastive manner so as to provide a robust pretrained model for fine-tuning the downstream joint classification task. Specifically, we design: 1) intersource and intrasource alignment loss (ER-Align) to achieve self-supervised feature extraction and alignment; 2) the source-unique feature adaptive separation (SUAS) strategy to model source-unique information explicitly; and 3) the auxiliary contrastive learning (ACL) strategy to mitigate the adverse impact of numerous false-negative samples in the pretraining stage. The experimental results and the theoretical analyses on multiple popular datasets comprehensively demonstrate the effectiveness and robustness of the proposed method under few-shot. Our code is available at: https://github.com/Xidian-AIGroup190726/CrossCLMP .

中文翻译：

---

具有改进的跨源对比学习的少样本 MS 和 PAN 联合分类


多光谱（MS）和全色（PAN）图像的联合分类旨在提供对土地特征更详细和准确的解释。尽管基于深度学习的方法在此任务中取得了显着的成功，但当标记样本不足时，网络的泛化性能会受到影响。在本研究中，我们探讨了通过对比学习利用未标记遥感图像 (RSI) 的可能性，并演示了将对比学习直接应用于 RSI 所面临的挑战。为此，我们提出了一种用于少样本 MS 和 PAN 联合分类的跨源对比学习方法（CrossCLMP），旨在以自监督对比方式学习足够的可转移表示，从而为精细分类提供鲁棒的预训练模型。 -调整下游联合分类任务。具体来说，我们设计：1）源间和源内对齐损失（ER-Align）以实现自监督的特征提取和对齐； 2）源独特特征自适应分离（SUAS）策略，用于显式建模源独特信息； 3）辅助对比学习（ACL）策略，以减轻预训练阶段大量假阴性样本的不利影响。对多个流行数据集的实验结果和理论分析全面证明了该方法在少样本情况下的有效性和鲁棒性。我们的代码位于：https://github.com/Xidian-AIGroup190726/CrossCLMP。