Explicit Change-Relation Learning for Change Detection in VHR Remote Sensing Images
Author:
Affiliation:
1. School of Computer Science and Engineering, Nanjing University of Science and Technology (NJUST), Nanjing, China
2. Center for Machine Vision and Security Research, Kennesaw State University (KSU), Kennesaw, GA, USA
Funder
National Natural Science Foundations of China
Jiangsu Provincial Natural Science Foundations of China
Fundamental Research Funds for the Central Universities
Key Projects of University Natural Science Fund of Jiangsu Province
Qinglan Project of Jiangsu Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/8859/10365397/10439252.pdf?arnumber=10439252
Reference16 articles.
1. SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images
2. Change vector analysis: An approach for detecting forest changes with Landsat;Malila
3. Remote sensing research issues of the National Land Use Change Program of China
4. Slow Feature Analysis for Change Detection in Multispectral Imagery
5. Fully Convolutional Siamese Networks for Change Detection
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1. Single-Stream Extractor Network With Contrastive Pre-Training for Remote-Sensing Change Captioning;IEEE Transactions on Geoscience and Remote Sensing;2024
2. Hyperspectral Target Detection: Learning Faithful Background Representations via Orthogonal Subspace-Guided Variational Autoencoder;IEEE Transactions on Geoscience and Remote Sensing;2024
3. Change-Agent: Toward Interactive Comprehensive Remote Sensing Change Interpretation and Analysis;IEEE Transactions on Geoscience and Remote Sensing;2024
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