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Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD).
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2013
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C. Wu, B. Du, and L. Zhang, “Slow feature analysis for change detection in multispectral imagery,” IEEE Trans. Geosci. Remote Sens. , vol. 52, no. 5, pp. 2858–2874, 2014
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 770–778, 2016
2016
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M. Gong, T. Zhan, P. Zhang, and Q. Miao, “Superpixel-based difference representation learning for change detection in multispectral remote sensing images,” IEEE Trans. Geosci. Remote Sens. , vol. 55, no. 5, pp. 2658–2673, 2017
2017
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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 2980–2988
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems (NIPS) , vol. 30, 2017
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international Conference on Computer Vision (ICCV) , 2017, pp. 2980–2988
2017
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2017
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R. Caye Daudt, B. Le Saux, and A. Boulch, “Fully convolutional siamese networks for change detection,” in Proceedings of the International Conference on Image Processing (ICIP) , 2018, pp. 4063–4067
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S. Elfwing, E. Uchibe, and K. Doya, “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,” Neural Netw. , vol. 107, pp. 3–11, 2018
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M. Berman, A. R. Triki, and M. B. Blaschko, “The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
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S. Ji, S. Wei, and M. Lu, “Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,” IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 1, pp. 574–586, 2018
2018
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H. Chen, C. Wu, B. Du, and L. Zhang, “Deep Siamese Multi-scale Convolutional Network for Change Detection in Multi-Temporal VHR Images,” in 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) , 2019, pp. 1–4
2019
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R. Caye Daudt, B. Le Saux, A. Boulch, and Y. Gousseau, “Multitask learning for large-scale semantic change detection,” Comput. Vis. Image Underst. , vol. 187, p. 102783, 2019
2019
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L. Mou, L. Bruzzone, and X. X. Zhu, “Learning spectral-spatialoral features via a recurrent convolutional neural network for change detection in multispectral imagery,” IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 2, pp. 924–935, 2019
2019
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S. Saha, F. Bovolo, and L. Bruzzone, “Unsupervised deep change vector analysis for multiple-change detection in VHR Images,” IEEE Trans. Geosci. Remote Sens. , vol. 57, no. 6, pp. 3677–3693, 2019
2019
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R. Gupta, B. Goodman, N. Patel, R. Hosfelt, S. Sajeev, E. Heim, J. Doshi, K. Lucas, H. Choset, and M. Gaston, “Creating xbd: A dataset for assessing building damage from satellite imagery,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2019
2019
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M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the International Conference on Machine Learning (ICML) , vol. 97, 2019, pp. 6105–6114
2019
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H. Chen, C. Wu, B. Du, L. Zhang, and L. Wang, “Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network,” IEEE Trans. Geosci. Remote Sens. , vol. 58, no. 4, pp. 2848–2864, 2020
2020
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C. Zhang, P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. Liu, “A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images,” ISPRS J. Photogramm. Remote Sens. , vol. 166, no. June, pp. 183–200, 2020
2020
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
H. Chen and Z. Shi, “A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,” Remote Sens. , vol. 12, no. 10, 2020
2020
Cited alongside, same era.
Z. Zheng, Y. Zhong, J. Wang, A. Ma, and L. Zhang, “Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters,” Remote Sens. Environ. , vol. 265, p. 112636, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Tian, Y. Zhong, Z. Zheng, A. Ma, X. Tan, and L. Zhang, “Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: From benchmark datasets to urban application,” ISPRS J. Photogramm. Remote Sens. , vol. 193, pp. 164–186, 2022
2022
Later among the works it cites.
2023
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H. Guo, Q. Shi, A. Marinoni, B. Du, and L. Zhang, “Deep building footprint update network: A semi-supervised method for updating existing building footprint from bi-temporal remote sensing images,” Remote Sens. Environ. , vol. 264, p. 112589, 2021
2021
Cited alongside, same era.
Y. Sun, L. Lei, D. Guan, and G. Kuang, “Iterative Robust Graph for Unsupervised Change Detection of Heterogeneous Remote Sensing Images,” IEEE Trans. Image Process. , vol. 30, pp. 6277–6291, 2021
2021
Cited alongside, same era.
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 Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” in Advances in Neural Information Processing Systems (NIPS) , vol. 34, 2021, pp. 12 077–12 090
2021
Cited alongside, same era.
2021
Cited alongside, same era.
D. Peng, L. Bruzzone, Y. Zhang, H. Guan, and P. He, “Scdnet: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery,” Int. J. Appl. Earth Obs. Geoinf. , vol. 103, p. 102465, 2021
2021
Cited alongside, same era.
Y. Cao and X. Huang, “A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 chinese cities,” Remote Sens. Environ. , vol. 264, p. 112590, 2021
2021
Cited alongside, same era.
H. Chen, N. Yokoya, C. Wu, and B. Du, “Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning,” IEEE Trans. Geosci. Remote Sens. , pp. 1–18, 2022
2022
Cited alongside, same era.
Later among the works it cites.
H. Chen, N. Yokoya, and M. Chini, “Fourier domain structural relationship analysis for unsupervised multimodal change detection,” ISPRS J. Photogramm. Remote Sens. , vol. 198, pp. 99–114, 2023
2023
Later among the works it cites.
Y. Cao and X. Huang, “A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels,” Remote Sens. Environ. , vol. 284, p. 113371, 2023
2023
Later among the works it cites.
Z. Lv, J. Liu, W. Sun, T. Lei, J. A. Benediktsson, and X. Jia, “Hierarchical attention feature fusion-based network for land cover change detection with homogeneous and heterogeneous remote sensing images,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–15, 2023
2023
Later among the works it cites.
K. Zhang, X. Zhao, F. Zhang, L. Ding, J. Sun, and L. Bruzzone, “Relation changes matter: Cross-temporal difference transformer for change detection in remote sensing images,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–15, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Han, C. Wu, H. Guo, M. Hu, and H. Chen, “Hanet: A hierarchical attention network for change detection with bitemporal very-high-resolution remote sensing images,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 16, pp. 3867–3878, 2023
2023
Later among the works it cites.
C. Han, C. Wu, H. Guo, M. Hu, J. Li, and H. Chen, “Change guiding network: Incorporating change prior to guide change detection in remote sensing imagery,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. , vol. 16, pp. 8395–8407, 2023
2023
Later among the works it cites.
S. Zhao, X. Zhang, P. Xiao, and G. He, “Exchanging dual-encoder–decoder: A new strategy for change detection with semantic guidance and spatial localization,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–16, 2023
2023
Later among the works it cites.
H. Chen, J. Song, C. Wu, B. Du, and N. Yokoya, “Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange,” ISPRS J. Photogramm. Remote Sens. , vol. 206, pp. 87–105, 2023
2023
Later among the works it cites.
S. Tian, X. Tan, A. Ma, Z. Zheng, L. Zhang, and Y. Zhong, “Temporal-agnostic change region proposal for semantic change detection,” ISPRS J. Photogramm. Remote Sens. , vol. 204, pp. 306–320, 2023
2023
Later among the works it cites.
Y. Niu, H. Guo, J. Lu, L. Ding, and D. Yu, “Smnet: Symmetric multi-task network for semantic change detection in remote sensing images based on cnn and transformer,” Remote Sens. , vol. 15, no. 4, 2023
2023
Later among the works it cites.
Y. Xiao, Q. Yuan, K. Jiang, J. He, C.-W. Lin, and L. Zhang, “Ttst: A top-k token selective transformer for remote sensing image super-resolution,” IEEE Trans. Image Process. , vol. 33, pp. 738–752, 2024
2024
Closest in time.
Y. Xiao, Q. Yuan, K. Jiang, J. He, X. Jin, and L. Zhang, “Ediffsr: An efficient diffusion probabilistic model for remote sensing image super-resolution,” IEEE Trans. Geosci. Remote Sens. , vol. 62, pp. 1–14, 2024
2024
Closest in time.
J. Song, H. Chen, and N. Yokoya, “Syntheworld: A large-scale synthetic dataset for land cover mapping and building change detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2024, pp. 8287–8296
2024
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2024
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2024
Closest in time.
L. Ding, J. Zhang, H. Guo, K. Zhang, B. Liu, and L. Bruzzone, “Joint spatio-temporal modeling for semantic change detection in remote sensing images,” IEEE Trans. Geosci. Remote Sens. , vol. 62, pp. 1–14, 2024
2024
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2024
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2024
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Y. Huang, X. Li, Z. Du, and H. Shen, “Spatiotemporal enhancement and interlevel fusion network for remote sensing images change detection,” IEEE Trans. Geosci. Remote Sens. , vol. 62, pp. 1–14, 2024
2024
Closest in time.