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Wang, Yi und Albrecht, Conrad M und Ait Ali Braham, Nassim und Mou, LiChao und Zhu, Xiao Xiang (2022) Self-supervised Learning in Remote Sensing: A Review. IEEE Geoscience and Remote Sensing Magazine (GRSM), 10 (4), Seiten 213-247. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/MGRS.2022.3198244. ISSN 2168-6831.
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Offizielle URL: https://ieeexplore.ieee.org/document/9875399
Kurzfassung
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.
| elib-URL des Eintrags: | https://elib.dlr.de/190040/ | ||||||||||||||||||||||||
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| Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
| Titel: | Self-supervised Learning in Remote Sensing: A Review | ||||||||||||||||||||||||
| Autoren: |
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| Datum: | Dezember 2022 | ||||||||||||||||||||||||
| Erschienen in: | IEEE Geoscience and Remote Sensing Magazine (GRSM) | ||||||||||||||||||||||||
| Referierte Publikation: | Ja | ||||||||||||||||||||||||
| Open Access: | Ja | ||||||||||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||||||||||
| In SCOPUS: | Ja | ||||||||||||||||||||||||
| In ISI Web of Science: | Ja | ||||||||||||||||||||||||
| Band: | 10 | ||||||||||||||||||||||||
| DOI: | 10.1109/MGRS.2022.3198244 | ||||||||||||||||||||||||
| Seitenbereich: | Seiten 213-247 | ||||||||||||||||||||||||
| Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||
| ISSN: | 2168-6831 | ||||||||||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||||||||||
| Stichwörter: | remote sensing, deep learning, self-supervised learning, earth observation | ||||||||||||||||||||||||
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
| HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
| HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||
| DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
| DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||
| DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||||||
| Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
| Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||||||
| Hinterlegt von: | Wang, Yi | ||||||||||||||||||||||||
| Hinterlegt am: | 14 Nov 2022 11:45 | ||||||||||||||||||||||||
| Letzte Änderung: | 25 Mai 2023 11:27 |
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