Self-supervised Learning in Remote Sensing: A Review
| Autor: | Yi Wang, Conrad M. Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
remote sensing General Computer Science Computer Vision and Pattern Recognition (cs.CV) self-supervised learning Computer Science - Computer Vision and Pattern Recognition General Earth and Planetary Sciences deep learning earth observation Electrical and Electronic Engineering Instrumentation |
| Popis: | 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. Accepted by IEEE Geoscience and Remote Sensing Magazine. 32 pages, 22 content pages |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|