Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works. (arXiv:2211.08129v1 [cs.CV])

Deep learning has achieved great success in learning features from massive
remote sensing images (RSIs). To better understand the connection between
feature learning paradigms (e.g., unsupervised feature learning (USFL),
supervised feature learning (SFL), and self-supervised feature learning
(SSFL)), this paper 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 RSIs understanding tasks and give …

arxiv challenges feature future remote sensing