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

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 2387 - 2402
• Main Authors: Sun, Xian, Wang, Bing, Wang, Zhirui, Li, Hao, Li, Hengchao, Fu, Kun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Bibliometrics; Data models; Deep generative model; Deep learning; few-shot learning; Image segmentation; Learning systems; Machine learning; meta-learning; metric learning; Neural networks; Object recognition; Remote sensing; Statistical analysis; Task analysis; Training; transfer learning
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2021.3052869

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.