Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works-Reference-Cited by-同舟云学术

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

Author:

Tao Chao¹^ORCID,Qi Ji¹^ORCID,Guo Mingning¹,Zhu Qing²^ORCID,Li Haifeng¹^ORCID

Affiliation:

1. School of Geosciences and Info-Physics, Central South University, Changsha, China

2. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China

Funder

Open Project of Xiangjiang Laboratory

National Natural Science Foundation of China

Natural Science Foundation of Hunan for Distinguished Young Scholars

Natural Science Foundation of Hunan

Inner Mongolia Science and Technology Plan

High-Performance Computing Center of Central South University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

Reference261 articles.

1. A large-scale study of representation learning with the visual task adaptation benchmark;zhai;arXiv 1910 04867,2019

5. Self-supervised learning is more robust to dataset imbalance;liu;Proc Adv Neural Inf Process Syst Workshop,2021

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