Deep neural network has been successfully applied to remote sensing image scene classification, which requires a large amount of annotated data for training. However, it is time-consuming and labor-intensive to obtain abundant labeled samples in various applications. Therefore, it is of great importance to conduct scene classification with only a few annotated images. In order to address the issue, we propose a task-specific contrastive learning (TSC) model for few-shot scene classification of remote sensing images, which aims to enhance the scene classification performance with fewer labeled samples. Specifically, a self-attention and mutual-attention module (SMAM) is developed to learn feature correlations and reduce the background interference. Moreover, a task-specific contrastive loss function is proposed to optimize the deep network, which generates task-specific paired data based on different views of original images. This strategy has a contribution to enhance the discrimination of features between intra-class and inter-class images. Experimental results on NWPU-RESISC45, WHU-RS19 and UCM datasets demonstrate that the proposed method produces superior accuracies compared with other related few-shot learning methods.

深度神经网络已成功应用于遥感图像场景分类，需要大量标注数据进行训练。然而，在各种应用中获得丰富的标记样本既费时又费力。因此，仅使用少量注释图像进行场景分类非常重要。为了解决这个问题，我们提出了一种任务特定的对比学习（TSC）模型，用于遥感图像的少镜头场景分类，旨在以更少的标记样本来提高场景分类性能。具体来说，开发了一个自注意和相互注意模块（SMAM）来学习特征相关性并减少背景干扰。此外，提出了一种特定于任务的对比损失函数来优化深度网络，它根据原始图像的不同视图生成特定于任务的配对数据。该策略有助于增强类内图像和类间图像之间的特征区分。在 NWPU-RESISC45、WHU-RS19 和 UCM 数据集上的实验结果表明，与其他相关的少样本学习方法相比，所提出的方法具有更高的准确性。