Dual-Stream Input Gabor Convolution Network for Building Change Detection in Remote Sensing Images
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5597-4_1
Reference19 articles.
1. Shafique, A., Cao, G., et al.: Deep learning-based change detection in remote sensing images: a review. Remote Sens. 14(4), 871 (2022)
2. Gu, Z., Zeng, M.: The use of artificial intelligence and satellite remote sensing in land cover change detection: review and perspectives. Sustainability 16(1), 274 (2024)
3. Basavaraju, K.S., Hiren, N.S., Sravya, N., et al.: BCDetNet: a deep learning architecture for building change detection from bi-temporal high resolution satellite images. Int. J. Mach. Learn. Cyber. 14, 4047–4062 (2023)
4. Eftekhari, A., Samadzadegan, F., et al.: Building change detection using the parallel spatial-channel attention block and edge-guided deep network. Int. J. Appl. Earth Obs. Geoinformation. 117, 103180 (2023)
5. Holail, S., Saleh, T., Xiao, X., Li, D.: AFDE-Net: building change detection using attention-based feature differential enhancement for satellite imagery. IEEE Geosci. Remote Sens. Lett. 20, 1–5 (2023)
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