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arXiv · 2206.13188

Self-supervised Learning in Remote Sensing: A Review

Yi Wang·Conrad M Albrecht·Nassim Ait Ali Braham·Lichao Mou·Xiao Xiang Zhu
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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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Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu. 2022-06-27. Self-supervised Learning in Remote Sensing: A Review. https://arxiv.org/abs/2206.13188

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