Self-Supervised Learning in Remote Sensing: A review

In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains loc...

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Bibliographic Details
Published inIEEE geoscience and remote sensing magazine Vol. 10; no. 4; pp. 213 - 247
Main Authors Wang, Yi, Albrecht, Conrad M., Braham, Nassim Ait Ali, Mou, Lichao, Zhu, Xiao Xiang
Format Journal Article
LanguageEnglish
Published IEEE 01.12.2022
Subjects
Deep learning
Learning systems
Remote sensing
Self-supervised learning
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Summary:In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.
ISSN:2473-2397
2168-6831
DOI:10.1109/MGRS.2022.3198244