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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- github.com/zhu-xlab/ssl4eo-reviewOfficialIn paperpytorch★ 38
- github.com/zhu-xlab/dino-mmpytorch★ 32
- github.com/hewanshrestha/why-self-supervision-in-timepytorch★ 0
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| EuroSAT | MoCo-v2 (ResNet18, fine tune) | Accuracy (%) | 98.9 | — | Unverified |
| EuroSAT | MoCo-v2 (ResNet18, linear eval) | Accuracy (%) | 94.4 | — | Unverified |