Urban flood mapping using Sentinel-1 and RADARSAT Constellation Mission image and convolutional Siamese network
Author:
Funder
Multi-Hazard Risk and Resilience
NSERC Alliance Grant
NSERC Discovery Grant
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06434-2.pdf
Reference42 articles.
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2. Bonafilia D, Tellman B, Anderson T, Issenberg E (2020) Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 210–211
3. Bouvet A, Mermoz S, Ballère M, Koleck T, Le Toan T (2018) Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. Remote Sens 10(8):1250
4. Chen C, Fan L (2021) Scene segmentation of remotely sensed images with data augmentation using U-net++. In: 2021 International conference on computer engineering and artificial intelligence (ICCEAI). IEEE, pp 201–205
5. Chen J, Yuan Z, Peng J, Chen L, Huang H, Zhu J, Liu Y, Li H (2020) DASNet: dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images. IEEE J Sel Top Appl Earth Obs Remote Sens 14:1194–1206
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