Self-Supervised Learning in Remote Sensing: A review

By Yi Wang, Conrad M. Albrecht, Nassim Ait Ali Braham, Lichao Mou, Xiao Xiang Zhu. 2022

357 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chao Tao
2. Haifeng Li
3. Ji Qi
4. Mingning Guo
5. Qing Zhu
6. Valerio Marsocci
7. Simone Scardapane
8. Jiale Zhu

In Computer Vision and Pattern Recognition

1. Haifeng Li
2. Chao Tao
3. Qing Zhu
4. Mingning Guo
5. Ji Qi
6. Valerio Marsocci
7. Simone Scardapane
8. Jiale Zhu

In Media Technology

1. Simone Scardapane
2. Valerio Marsocci
3. Ji Qi
4. Haifeng Li
5. Jiale Zhu
6. Qing Zhu
7. Jun Zhou
8. Mingning Guo

Citations in the corpus

• Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, methodology): “In computer vision (CV), SSL has been proposed to handle this problem, reducing the amount of annotated data needed [3], [4].”
• RemoteCLIP: A Vision Language Foundation Model for Remote Sensing (2023, background): “The research landscape of SSL-based foundation models for remote sensing closely mirrors this categorization [31].”
• Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022, methodology): “[42] summarized some representative methods of SSFL and analyzed their application in remote sensing tasks.”