DeepAI AI Chat
Log In Sign Up

Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works

by   Chao Tao, et al.
Central South University

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 a comprehensive review of the existing SSFL work in RS, including the pre-training dataset, self-supervised feature learning signals, and the evaluation methods. We further analyze the effect of SSFL signals and pre-training data on the learned features to provide insights for improving the RSI feature learning. Finally, we briefly discuss some open problems and possible research directions.


page 1

page 4

page 6

page 7

page 8

page 9

page 12


Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding

In recent years self-supervised learning has emerged as a promising cand...

A Theory of Feature Learning

Feature Learning aims to extract relevant information contained in data ...

Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images

Self-Supervised learning (SSL) has become the new state-of-art in severa...

Self-Supervised Representation Learning: Introduction, Advances and Challenges

Self-supervised representation learning methods aim to provide powerful ...

Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate

Most galaxies in the nearby Universe are gravitationally bound to a clus...

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

Visual aesthetic assessment has been an active research field for decade...