Self-supervised Learning in Remote Sensing: A Review
It addresses the underutilization of SSL in remote sensing for researchers and practitioners, but is incremental as it reviews and benchmarks existing methods.
The paper reviews self-supervised learning (SSL) concepts and developments in computer vision for remote sensing, providing a benchmark on popular datasets to verify SSL's potential and identify future research directions.
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.