A CNN-BASED FLOOD MAPPING APPROACH USING SENTINEL-1 DATA

9 citations as recorded by crossref.

1. Evaluation of typhoon-induced inundation losses associated with LULC using multi-temporal SAR and optical images J. Wang et al. 10.1080/19475705.2022.2112624
2. Floodsar: Automatic mapping of river flooding extent from multitemporal SAR imagery T. Berezowski et al. 10.1016/j.softx.2024.101717
3. XFIMNet: an Explainable deep learning architecture for versatile flood inundation mapping with synthetic aperture radar and multi-spectral optical images J. Sanderson et al. 10.1080/01431161.2023.2288945
4. Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP) P. Gautam et al. 10.1007/s12517-024-11910-x
5. Segmentation and Visualization of Flooded Areas Through Sentinel-1 Images and U-Net F. Pech-May et al. 10.1109/JSTARS.2024.3387452
6. Deep attentive fusion network for flood detection on uni-temporal Sentinel-1 data R. Yadav et al. 10.3389/frsen.2022.1060144
7. Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data S. Surampudi & V. Kumar 10.1016/j.rsase.2024.101361
8. Supervised and unsupervised machine learning approaches using Sentinel data for flood mapping and damage assessment in Mozambique M. Nhangumbe et al. 10.1016/j.rsase.2023.101015
9. SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks K. Halder et al. 10.1080/19475705.2024.2409202