Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation

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.

Automated summary

This article provides a bibliometric analysis of existing works on remote sensing interpretation related to few-shot learning, introduces two categories of few-shot learning methods, and lists three typical remote sensing interpretation applications with corresponding datasets and evaluation criteria. It summarizes the research status and suggests possible research directions for scholars in the field of remote sensing and few-shot learning.