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Self-supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works

Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between three feature learning paradigms, which are unsupervised feature learning (USFL), supervised feature learning (SFL), and self-supervised feature learni...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Main Authors: Tao, Chao, Qi, Ji, Guo, Mingning, Zhu, Qing, Li, Haifeng
Format: Article
Language:English
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Summary:Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between three feature learning paradigms, which are unsupervised feature learning (USFL), supervised feature learning (SFL), and self-supervised feature learning (SSFL), this paper analyzes and compares them from the perspective of feature learning signals, and gives a unified feature learning framework. Under this unified framework, we analyze the advantages of SSFL over the other two learning paradigms in RSI understanding tasks and give a comprehensive review of existing SSFL works in RS, including the pre-training dataset, self-supervised feature learning signals, and the evaluation methods. We further analyze the effects of SSFL signals and pre-training data on the learned features to provide insights into RSI feature learning. Finally, we briefly discuss some open problems and possible research directions.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3276853