Full-Range Fusion Network with Local-Global Attention for Change Detection in Remote Sensing Images
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5600-1_41
Reference12 articles.
1. Bin, Y.A.N.G., et al.: Review of remote sensing change detection in deep learning: bibliometric and analysis. Nat. Remote Sens. Bull. 27(9), 1988–2005 (2023)
2. Fang, S., et al.: SNUNet-CD: a densely connected Siamese network for change detection of VHR images. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)
3. Chen, H., Shi, Z.: A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens. 12(10), 1662 (2020)
4. Chen, J., et al.: DASNet: dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 14, 1194–1206 (2020)
5. Shi, Q., et al.: A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2021)
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