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Deep learning techniques have achieved great success in remote sensing image change detection.
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M. Gong, X. Niu, P. Zhang, and Z. Li, “Generative adversarial networks for change detection in multispectral imagery,” IEEE Geoscience and Remote Sensing Letters , vol. 14, no. 12, pp. 2310–2314, 2017
2017
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X. Niu, M. Gong, T. Zhan, and Y. Yang, “A conditional adversarial network for change detection in heterogeneous images,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 1, pp. 45–49, 2018
2018
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N. Lv, C. Chen, T. Qiu, and A. K. Sangaiah, “Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in sar images,” IEEE transactions on industrial informatics , vol. 14, no. 12, pp. 5530–5538, 2018
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Z. Zhang, Q. Liu, and Y. Wang, “Road extraction by deep residual u-net,” IEEE Geoscience and Remote Sensing Letters , vol. 15, no. 5, pp. 749–753, 2018
2018
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R. C. Daudt, B. Le Saux, A. Boulch, and Y. Gousseau, “Urban change detection for multispectral earth observation using convolutional neural networks,” in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2018, pp. 2115–2118
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Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
L. Bergamasco, S. Saha, F. Bovolo, and L. Bruzzone, “Unsupervised change-detection based on convolutional-autoencoder feature extraction,” in Image and Signal Processing for Remote Sensing XXV , vol. 11155. International Society for Optics and Photonics, 2019, p. 1115510
2019
H. Dong, W. Ma, Y. Wu, J. Zhang, and L. Jiao, “Self-supervised representation learning for remote sensing image change detection based on temporal prediction,” Remote Sensing , vol. 12, no. 11, p. 1868, 2020
2020
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2020
Later among the works it cites.
2020
Later among the works it cites.
M. Poggi, F. Aleotti, F. Tosi, and S. Mattoccia, “On the uncertainty of self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3227–3237
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Cited alongside, same era.
E. Kalinicheva, J. Sublime, and M. Trocan, “Change detection in satellite images using reconstruction errors of joint autoencoders,” in International Conference on Artificial Neural Networks . Springer, 2019, pp. 637–648
2019
Cited alongside, same era.
2020
Cited alongside, same era.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
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T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
C. Ren, X. Wang, J. Gao, X. Zhou, and H. Chen, “Unsupervised change detection in satellite images with generative adversarial network,” IEEE Transactions on Geoscience and Remote Sensing , 2020
2020
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Z.-G. Liu, Z.-W. Zhang, Q. Pan, and L.-B. Ning, “Unsupervised change detection from heterogeneous data based on image translation,” IEEE Transactions on Geoscience and Remote Sensing , 2021
2021
Closest in time.
2021
Closest in time.
Y. Shen, Z. Zhang, M. R. Sabuncu, and L. Sun, “Real-time uncertainty estimation in computer vision via uncertainty-aware distribution distillation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 707–716
2021
Closest in time.