An Empirical Study of Remote Sensing Pretraining-Reference-Cited by-同舟云学术

An Empirical Study of Remote Sensing Pretraining

Published:2023 Issue: Volume:61 Page:1-20
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wang Di¹^ORCID,Zhang Jing²^ORCID,Du Bo³^ORCID,Xia Gui-Song⁴^ORCID,Tao Dacheng¹^ORCID

Affiliation:

1. JD Explore Academy, Beijing, China

2. Faculty of Engineering, School of Computer Science, The University of Sydney, Sydney, NSW, Australia

3. School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Wuhan University, Wuhan, China

4. School of Computer Science, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

Science and Technology Major Project of Hubei Province

ARC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/09782149.pdf?arnumber=9782149

Reference154 articles.

1. Masked autoencoders are scalable vision learners;he;arXiv 2111 06377,2021

2. RegionCL: Can simple region swapping contribute to contrastive learning?;xu;arXiv 2111 12309,2021

3. Geography-Aware Self-Supervised Learning

4. Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

5. Geographical Knowledge-Driven Representation Learning for Remote Sensing Images

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