CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection-Reference-Cited by-同舟云学术

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CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection

Published:2024 Issue: Volume:17 Page:2133-2148
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wan Ling¹^ORCID,Tian Ye¹^ORCID,Kang Wenchao¹^ORCID,Ma Lei¹^ORCID

Affiliation:

1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

Research Funding of Satellite Information Intelligent Processing and Application Research Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10296521.pdf?arnumber=10296521

Reference39 articles.

1. Review Article Digital change detection techniques using remotely-sensed data

2. Automatic extraction of flood inundation areas from SAR images: a case study of Jilin, China during the 2017 flood disaster

3. Changer: Feature Interaction is What You Need for Change Detection

4. Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering

5. A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain

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