SEDANet: A New Siamese Ensemble Difference Attention Network for Building Change Detection in Remotely Sensed Images-Reference-Cited by-同舟云学术

SEDANet: A New Siamese Ensemble Difference Attention Network for Building Change Detection in Remotely Sensed Images

Published:2024 Issue: Volume:62 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Yang Yue¹,Chen Tao¹^ORCID,Lei Tao²^ORCID,Du Bo³^ORCID,Nandi Asoke K.⁴^ORCID,Plaza Antonio⁵^ORCID

Affiliation:

1. School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China

2. School of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi’an, China

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

4. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

5. Department of Technology of Computers and Communications, Hyperspectral Computing Laboratory, Escuela Politécnica, University of Extremadura, Cáceres, Spain

Funder

National Natural Science Foundation of China

Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection

Open Fund of State Key Laboratory of Remote Sensing Science

Open Fund of Badong National Observation and Research Station of Geohazards

Opening Fund of the Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10478952.pdf?arnumber=10478952

Reference71 articles.

1. Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing

2. Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery

3. MFE-ResNet: A new extraction framework for land cover characterization in mining areas

4. Multi-temporal image analysis for LULC classification and change detection

5. Iterative Training Sample Augmentation for Enhancing Land Cover Change Detection Performance With Deep Learning Neural Network

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