Self-supervised Learning in Remote Sensing: A Review

Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu
6/27/2022
cs.CV

Official code: zhu-xlab/SSL4EO-Review (38 stars, last commit 2023-02-24).

Abstract

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 a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper, 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 fruitful interaction of both domains.

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Code Implementations(1)

zhu-xlab/SSL4EO-ReviewOfficial100%

Codes for the benchmark experiments in the paper "Self-supervised Learning in Remote Sensing: A Review"

383Shell, PythonJun 17, 20223 years ago

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Cite this paper

@article{wang2022selfsupervised,
  title  = {Self-supervised Learning in Remote Sensing: A Review},
  author = {Yi Wang and Conrad M Albrecht and Nassim Ait Ali Braham and Lichao Mou and Xiao Xiang Zhu},
  year   = {2022},
  eprint = {2206.13188},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2206.13188}
}

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