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Recently, change detection (CD) of remote sensing images have achieved great progress with the advances of deep learning.
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Alcantarilla, P.F., Stent, S., Ros, G., Arroyo, R., Gherardi, R.: Street-view change detection with deconvolutional networks. Autonomous Robots 42
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Daudt, R.C., Le Saux, B., Boulch, A.: Fully convolutional siamese networks for change detection. In: IEEE International Conference on Image Processing. pp. 4063–4067. IEEE (2018)
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Ji, S., Wei, S., Lu, M.: Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Transactions on Geoscience and Remote Sensing 57
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Zhang, P., Liu, W., Wang, D., Lei, Y., Wang, H., Lu, H.: Non-rigid object tracking via deep multi-scale spatial-temporal discriminative saliency maps. Pattern Recognition 100
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Chen, H., Qi, Z., Shi, Z.: Remote sensing image change detection with transformers. IEEE Transactions on Geoscience and Remote Sensing 60
2021
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Fang, S., Li, K., Shao, J., Li, Z.: Snunet-cd: A densely connected siamese network for change detection of vhr images. IEEE Geoscience and Remote Sensing Letters 19
2021
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Liu, M., Shi, Q., Marinoni, A., He, D., Liu, X., Zhang, L.: Super-resolution-based change detection network with stacked attention module for images with different resolutions. IEEE Transactions on Geoscience and Remote Sensing 60
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Lv, N., Chen, C., Qiu, T., Sangaiah, A.K.: Deep learning and superpixel feature extraction based on contractive autoencoder for change detection in sar images. IEEE Transactions on Industrial Informatics 14
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2018
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Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3–11. Springer (2018)
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Lei, Y., Liu, X., Shi, J., Lei, C., Wang, J.: Multiscale superpixel segmentation with deep features for change detection. IEEE Access 7
2019
Cited alongside, same era.
Peng, D., Guan, H.: Unsupervised change detection method based on saliency analysis and convolutional neural network. Journal of Applied Remote Sensing 13
2019
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Peng, D., Zhang, Y., Guan, H.: End-to-end change detection for high resolution satellite images using improved unet++. Remote Sensing 11
2019
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Saha, S., Bovolo, F., Bruzzone, L.: Unsupervised deep change vector analysis for multiple-change detection in vhr images. IEEE Transactions on Geoscience and Remote Sensing 57
2019
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Tan, K., Zhang, Y., Wang, X., Chen, Y.: Object-based change detection using multiple classifiers and multi-scale uncertainty analysis. Remote Sensing 11
2019
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2021
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)
2021
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Shi, Q., Liu, M., Li, S., Liu, X., Wang, F., Zhang, L.: A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection. IEEE Transactions on Geoscience and Remote Sensing 60
2021
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Wang, Y., Xu, Z., Wang, X., Shen, C., Cheng, B., Shen, H., Xia, H.: End-to-end video instance segmentation with transformers. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 8741–8750 (2021)
2021
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Wang, Z., Zhang, Y., Luo, L., Wang, N.: Transcd: scene change detection via transformer-based architecture. Optics Express 29
2021
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2021
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Zhang, G., Zhang, P., Qi, J., Lu, H.: Hat: Hierarchical aggregation transformers for person re-identification. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 516–525 (2021)
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2022
Closest in time.
He, X., Tan, E.L., Bi, H., Zhang, X., Zhao, S., Lei, B.: Fully transformer network for skin lesion analysis. Medical Image Analysis 77
2022
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Ke, Q., Zhang, P.: Hybrid-transcd: A hybrid transformer remote sensing image change detection network via token aggregation. ISPRS International Journal of Geo-Information 11
2022
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Li, Q., Zhong, R., Du, X., Du, Y.: Transunetcd: A hybrid transformer network for change detection in optical remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing 60
2022
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Song, F., Zhang, S., Lei, T., Song, Y., Peng, Z.: Mstdsnet-cd: Multiscale swin transformer and deeply supervised network for change detection of the fast-growing urban regions. IEEE Geoscience and Remote Sensing Letters 19
2022
Closest in time.
Wang, G., Li, B., Zhang, T., Zhang, S.: A network combining a transformer and a convolutional neural network for remote sensing image change detection. Remote Sensing 14
2022
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Zhang, C., Wang, L., Cheng, S., Li, Y.: Swinsunet: Pure transformer network for remote sensing image change detection. IEEE Transactions on Geoscience and Remote Sensing 60
2022
Closest in time.
Zheng, Z., Zhong, Y., Tian, S., Ma, A., Zhang, L.: Changemask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection. ISPRS Journal of Photogrammetry and Remote Sensing 183
2022
Closest in time.