Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works

By Chao Tao, Ji Qi, Mingning Guo, Qing Zhu, Haifeng Li. 2022

91 citations recorded by the source corpus.

Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between three feature learning paradigms, which are unsupervised feature learning (USFL), supervised feature learning (SFL), and self-SFL (SSFL), this article analyzes and compares them from the perspective of feature learning signals and gives a unified feature learning framework. Under this unified framework, we analyze the advantages of SSFL over the other two learning paradigms in RSI understanding tasks and give a comprehensive review of existing SSFL works in RS, including the pretraining dataset, SSFL signals, and evaluation methods. We further analyze the effects of SSFL signals and pretraining data on the learned features to provide insights into RSI feature learning. Finally, we briefly discuss some open problems and possible research directions.

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. Valerio Marsocci
2. Simone Scardapane
3. Laura Palagi

In Computer Vision and Pattern Recognition

1. Valerio Marsocci
2. Simone Scardapane
3. Laura Palagi

In Media Technology

1. Simone Scardapane
2. Valerio Marsocci

Citations in the corpus

• Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, background): “In [11] and [12], we can find two reviews of SSL in RS.”