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Yi Wang Conrad M Albrecht Nassim Ait Ali Braham Lichao Mou Xiao Xiang Zhu
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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| image-classification-on-eurosat | MoCo-v2 (ResNet18, linear eval) | Accuracy (%): 94.4 |
| image-classification-on-eurosat | MoCo-v2 (ResNet18, fine tune) | Accuracy (%): 98.9 |
| multi-label-image-classification-on | MoCo-v2 (ResNet18, fine tune) | mAP (micro): 89.3 official split: No |
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