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Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images.
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Cited alongside, same era.
J. Chen, Z. Yuan, J. Peng, L. Chen, H. Huang, J. Zhu, Y. Liu, and H. Li, “Dasnet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images,”
2021
Cited alongside, same era.
M. Papadomanolaki, M. Vakalopoulou, and K. Karantzalos, “A deep multitask learning framework coupling semantic segmentation and fully convolutional lstm networks for urban change detection,”
2021
Cited alongside, same era.
B. Bai, W. Fu, T. Lu, and S. Li, “Edge-Guided Recurrent Convolutional Neural Network for Multitemporal Remote Sensing Image Building Change Detection,”
2021
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O. Manas, A. Lacoste, X. G. i Nieto, D. Vazquez, and P. Rodriguez, “Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data,” in
2021
Cited alongside, same era.
S. Fang, K. Li, J. Shao, and Z. Li, “Snunet-cd: A densely connected siamese network for change detection of vhr images,”
2021
Cited alongside, same era.
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Y. Chen, S. Liu, and X. Wang, “Learning continuous image representation with local implicit image function,” in
2021
Cited alongside, same era.
J. Lei, Y. Gu, W. Xie, Y. Li, and Q. Du, “Boundary extraction constrained siamese network for remote sensing image change detection,”
2022
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J. Liu, W. Xuan, Y. Gan, Y. Zhan, J. Liu, and B. Du, “An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection,”
2022
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2022
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H. Chen, W. Li, S. Chen, and Z. Shi, “Semantic-aware dense representation learning for remote sensing image change detection,”
2022
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2022
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J. Pan, W. Cui, X. An, X. Huang, H. Zhang, S. Zhang, R. Zhang, X. Li, W. Cheng, and Y. Hu, “MapsNet: Multi-level feature constraint and fusion network for change detection,”
2022
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W. Wang, X. Tan, P. Zhang, and X. Wang, “A cbam based multiscale transformer fusion approach for remote sensing image change detection,”
2022
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N. Shi, K. Chen, and G. Zhou, “A divided spatial and temporal context network for remote sensing change detection,”
2022
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X. Song, Z. Hua, and J. Li, “Pstnet: Progressive sampling transformer network for remote sensing image change detection,”
2022
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Q. Ke and P. Zhang, “Hybrid-TransCD: A Hybrid Transformer Remote Sensing Image Change Detection Network via Token Aggregation,”
2022
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2022
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T. Shen, Y. Zhang, L. Qi, J. Kuen, X. Xie, J. Wu, Z. Lin, and J. Jia, “High quality segmentation for ultra high-resolution images,” in
2022
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2022
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2022
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2022
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H. Zheng, M. Gong, T. Liu, F. Jiang, T. Zhan, D. Lu, and M. Zhang, “Hfa-net: High frequency attention siamese network for building change detection in vhr remote sensing images,”
2022
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2022
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A. Toker, L. Kondmann, M. Weber, M. Eisenberger, A. Camero, J. Hu, A. P. Hoderlein, Ç. Senaras, T. Davis, D. Cremers, G. Marchisio, X. X. Zhu, and L. Leal-Taixé, “Dynamicearthnet: Daily multi-spectral satellite dataset for semantic change segmentation,” in
2022
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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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2023
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L. Liu, Z. Zou, and Z. Shi, “Hyperspectral remote sensing image synthesis based on implicit neural spectral mixing models,”
2023
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2023
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J. Luo, L. Han, X. Gao, X. Liu, and W. Wang, “Sr-feinr: Continuous remote sensing image super-resolution using feature-enhanced implicit neural representation,”
2023
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K. Chen, W. Li, J. Chen, Z. Zou, and Z. Shi, “Resolution-agnostic remote sensing scene classification with implicit neural representations,”
2023
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Y. Shangguan, J. Li, and Z. Hua, “Contour-enhanced densely connected siamese network for change detection,”
2023
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J. Tian, D. Peng, H. Guan, and H. Ding, “RACDNet: Resolution- and alignment-aware change detection network for optical remote sensing imagery,”
2072
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R. Shao, C. Du, H. Chen, and J. Li, “SUNet: Change detection for heterogeneous remote sensing images from satellite and UAV using a dual-channel fully convolution network,”
2072
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