The rapid development of deep learning brings effective solutions for remote sensing image interpretation. Training deep neural network models usually require a large number of manually labeled samples. However, there is a limitation to obtain sufficient labeled samples in remote sensing field to satisfy the data requirement. Therefore, it is of great significance to conduct the research on few-shot learning for remote sensing image interpretation. First, this article provides a bibliometric analysis of the existing works for remote sensing interpretation related to few-shot learning. Second, two categories of few-shot learning methods, i.e., the data-augmentation-based and the prior-knowledge-based, are introduced for the interpretation of remote sensing images. Then, three typical remote sensing interpretation applications are listed, including scene classification, semantic segmentation, and object detection, together with the corresponding public datasets and the evaluation criteria. Finally, the research status is summarized, and some possible research directions are provided. This article gives a reference for scholars working on few-shot learning research in the remote sensing field.

中文翻译：

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遥感影像少生学习研究进展

深度学习的飞速发展为遥感影像解释带来了有效的解决方案。训练深度神经网络模型通常需要大量的手动标记样本。然而，在遥感领域中获得足够的标记样本以满足数据需求是有局限性的。因此，开展少拍学习对遥感影像解释具有重要意义。首先，本文对与少拍学习相关的遥感解释的现有作品进行了文献计量分析。其次，介绍了两类短镜头学习方法，即基于数据增强和基于先验知识的方法，用于解释遥感图像。然后，列出了三种典型的遥感解释应用程序，包括场景分类，语义分割和对象检测，以及相应的公共数据集和评估标准。最后，总结了研究现状，并提供了一些可能的研究方向。本文为从事遥感领域少拍学习研究的学者提供了参考。