Multicue Contrastive Self-Supervised Learning for Change Detection in Remote Sensing-Reference-Cited by-同舟云学术

Multicue Contrastive Self-Supervised Learning for Change Detection in Remote Sensing

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Yang Meijuan¹^ORCID,Jiao Licheng²^ORCID,Liu Fang²^ORCID,Hou Biao²^ORCID,Yang Shuyuan²^ORCID,Zhang Yake²^ORCID,Wang Jianlong³^ORCID

Affiliation:

1. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China

2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China

3. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China

Funder

National Natural Science Foundation of China

State Key Program of the National Natural Science Foundation of China

General Program of the National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10313304.pdf?arnumber=10313304

Reference57 articles.

1. Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

2. DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

3. Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis

4. Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise-Based Markov Random Field Model

5. A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images

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