Self-supervised Learning in Remote Sensing: A Review

27 Jun 2022  ·  Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu ·

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.

PDF Abstract
Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Multi-Label Image Classification BigEarthNet MoCo-v2 (ResNet18, fine tune) mAP (micro) 89.3 # 3
official split No # 1
Image Classification EuroSAT MoCo-v2 (ResNet18, fine tune) Accuracy (%) 98.9 # 4
Image Classification EuroSAT MoCo-v2 (ResNet18, linear eval) Accuracy (%) 94.4 # 10


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