Abstract
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.
Abstract (translated)
少样本遥感图像场景分类（FS-RSISC）旨在仅使用少量标注样本对遥感图像进行分类。其主要挑战在于类间差异小、类内差异大，这是遥感图像的固有特性。为应对这些挑战，我们提出了一种基于迁移的双对比网络（DCN），该网络在训练过程中集成了两个辅助监督对比学习分支。具体而言，其中一个分支为上下文引导对比学习（CCL）分支，另一个为细节引导对比学习（DCL）分支，二者分别聚焦于类间判别性与类内不变性。在CCL分支中，我们首先设计了冷凝器网络以捕获上下文特征，随后在获得的上下文特征上应用监督对比学习，从而促进模型学习更具判别性的特征。在DCL分支中，我们设计了熔炉网络以凸显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，以充分挖掘各特征图的空间信息，使模型能够集中于不变的细节特征。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具有竞争力的性能。
URL
https://arxiv.org/abs/2603.23161
