Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote sensing images with only a few labeled samples. The main challenges lie in small inter-class variances and large intra-class variances, which are the inherent property of remote sensing images. To address these challenges, we propose a transfer-based Dual Contrastive Network (DCN), which incorporates two auxiliary supervised contrastive learning branches during the training process. Specifically, one is a Context-guided Contrastive Learning (CCL) branch and the other is a Detail-guided Contrastive Learning (DCL) branch, which focus on inter-class discriminability and intra-class invariance, respectively. In the CCL branch, we first devise a Condenser Network to capture context features, and then leverage a supervised contrastive learning on top of the obtained context features to facilitate the model to learn more discriminative features. In the DCL branch, a Smelter Network is designed to highlight the significant local detail information. And then we construct a supervised contrastive learning based on the detail feature maps to fully exploit the spatial information in each map, enabling the model to concentrate on invariant detail features. Extensive experiments on four public benchmark remote sensing datasets demonstrate the competitive performance of our proposed DCN.

翻译：少样本遥感图像场景分类（FS-RSISC）旨在仅利用少量标注样本对遥感图像进行分类。其主要挑战在于遥感图像固有的类间方差小、类内方差大的特性。为解决这些问题，我们提出一种基于迁移的双对比网络（DCN），该网络在训练过程中引入两个辅助式监督对比学习分支。具体而言，一个是上下文引导对比学习（CCL）分支，另一个是细节引导对比学习（DCL）分支，分别关注类间判别性和类内不变性。在CCL分支中，我们首先设计了一个冷凝网络来捕获上下文特征，然后利用所得上下文特征上的监督对比学习，促使模型学习更具判别性的特征。在DCL分支中，我们设计了一个熔炼网络以突出显著的局部细节信息，并基于细节特征图构建监督对比学习，充分挖掘每张特征图中的空间信息，使模型聚焦于不变细节特征。在四个公开基准遥感数据集上的大量实验表明，我们提出的DCN具有竞争性性能。