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

By Xian Sun, Bing Wang, Zhirui Wang, Hao Li, Hengchao Li, Kun Fu. 2021

212 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, background): “to rely on a labeled dataset, in light of the high cost and the amount of effort and time that are required, along with domain expertise [6].”
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot (2023, methodology): “This is achieved through "prompt engineering," where prompts are carefully designed to guide the model toward generating the desired output [29, 48].”
Anchor-Free Oriented Proposal Generator for Object Detection (2021, background): “Recently, computer vision has rapidly grown with the help of deep learning technology [8]–[14].”