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Wang, Yi and Albrecht, Conrad M and Ait Ali Braham, Nassim and Mou, LiChao and Zhu, Xiao Xiang (2022) Self-supervised Learning in Remote Sensing: A Review. IEEE Geoscience and Remote Sensing Magazine (GRSM), 10 (4), pp. 213-247. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/MGRS.2022.3198244. ISSN 2168-6831.
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Official URL: https://ieeexplore.ieee.org/document/9875399
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 big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, 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 the fruitful interaction of both domains.
| Item URL in elib: | https://elib.dlr.de/190040/ | ||||||||||||||||||||||||
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| Document Type: | Article | ||||||||||||||||||||||||
| Title: | Self-supervised Learning in Remote Sensing: A Review | ||||||||||||||||||||||||
| Authors: |
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| Date: | December 2022 | ||||||||||||||||||||||||
| Journal or Publication Title: | IEEE Geoscience and Remote Sensing Magazine (GRSM) | ||||||||||||||||||||||||
| Refereed publication: | Yes | ||||||||||||||||||||||||
| Open Access: | Yes | ||||||||||||||||||||||||
| Gold Open Access: | No | ||||||||||||||||||||||||
| In SCOPUS: | Yes | ||||||||||||||||||||||||
| In ISI Web of Science: | Yes | ||||||||||||||||||||||||
| Volume: | 10 | ||||||||||||||||||||||||
| DOI: | 10.1109/MGRS.2022.3198244 | ||||||||||||||||||||||||
| Page Range: | pp. 213-247 | ||||||||||||||||||||||||
| Publisher: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||
| ISSN: | 2168-6831 | ||||||||||||||||||||||||
| Status: | Published | ||||||||||||||||||||||||
| Keywords: | remote sensing, deep learning, self-supervised learning, earth observation | ||||||||||||||||||||||||
| HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||||||
| HGF - Program: | Space | ||||||||||||||||||||||||
| HGF - Program Themes: | Earth Observation | ||||||||||||||||||||||||
| DLR - Research area: | Raumfahrt | ||||||||||||||||||||||||
| DLR - Program: | R EO - Earth Observation | ||||||||||||||||||||||||
| DLR - Research theme (Project): | R - Artificial Intelligence | ||||||||||||||||||||||||
| Location: | Oberpfaffenhofen | ||||||||||||||||||||||||
| Institutes and Institutions: | Remote Sensing Technology Institute > EO Data Science | ||||||||||||||||||||||||
| Deposited By: | Wang, Yi | ||||||||||||||||||||||||
| Deposited On: | 14 Nov 2022 11:45 | ||||||||||||||||||||||||
| Last Modified: | 25 May 2023 11:27 |
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