Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images-Reference-Cited by-同舟云学术

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Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

Published:2022 Issue: Volume:60 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:

Ding Lei¹^ORCID,Guo Haitao¹,Liu Sicong²^ORCID,Mou Lichao³^ORCID,Zhang Jing⁴,Bruzzone Lorenzo⁴^ORCID

Affiliation:

1. PLA Strategic Force Information Engineering University, Zhengzhou, China

2. College of Surveying and Geoinformatics, Tongji University, Shanghai, China

3. Remote Sensing Technology Institute, German Aerospace Center, Wßling, Germany

4. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

Funder

National Natural Science Foundation of China

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09721305.pdf?arnumber=9721305

Reference63 articles.

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

2. Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images

3. Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

4. Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

5. The Spectral-Spatial Joint Learning for Change Detection in Multispectral Imagery

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