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Change detection in remote sensing images is essential for tracking environmental changes on the Earth's surface.
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2018
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2018
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2018
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2020
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2020
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2021
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2021
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Z. Zheng, A. Ma, L. Zhang, and Y. Zhong, “Change is everywhere: Single-temporal supervised object change detection in remote sensing imagery,” in
2021
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H. Chen, Z. Qi, and Z. Shi, “Remote sensing image change detection with transformers,”
2021
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2021
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2022
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2022
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2023
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Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in
2021
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W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in
2021
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2021
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M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in
2021
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark
2021
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2021
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S. Fang, K. Li, J. Shao, and Z. Li, “Snunet-cd: A densely connected siamese network for change detection of vhr images,”
2021
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R. Zhang, H. Zhang, X. Ning, X. Huang, J. Wang, and W. Cui, “Global-aware siamese network for change detection on remote sensing images,”
2023
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2023
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2023
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2023
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2023
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2023
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2023
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S. Fang, K. Li, and Z. Li, “Changer: Feature interaction is what you need for change detection,”
2023
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Y. Feng, J. Jiang, H. Xu, and J. Zheng, “Change detection on remote sensing images using dual-branch multilevel intertemporal network,”
2023
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J. Zhang, Z. Shao, Q. Ding, X. Huang, Y. Wang, X. Zhou, and D. Li, “Aernet: An attention-guided edge refinement network and a dataset for remote sensing building change detection,”
2023
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2023
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J. Yao, X. Wang, S. Yang, and B. Wang, “Vitmatte: Boosting image matting with pre-trained plain vision transformers,”
2024
Closest in time.
J. Ma, J. Duan, X. Tang, X. Zhang, and L. Jiao, “Eatder: Edge-assisted adaptive transformer detector for remote sensing change detection,”
2024
Closest in time.