A NOVEL HYBRID MODEL BASED ON CNN AND MULTI-SCALE TRANSFORMER FOR EXTRACTING WATER BODIES FROM HIGH RESOLUTION REMOTE SENSING IMAGES

6 citations as recorded by crossref.

1. Breaking the Scale Barrier: A Scale-Progressive Water Body Extraction Framework Inspired by Human Cognitive Mechanisms F. Meng et al. https://doi.org/10.1109/TGRS.2026.3661185
2. DMLU-Net: A Hybrid Neural Network for Water Body Extraction from Remote Sensing Images Z. Xu et al. https://doi.org/10.3390/app15147733
3. YOLO-MRAF: A multi-scale reinforced attention fusion network for waterbody segmentation T. Zhou et al. https://doi.org/10.1007/s44443-026-01032-7
4. Emerging methodologies in waterbody delineation: an In-depth review S. Rajeswari & P. Rathika https://doi.org/10.1080/01431161.2024.2379518
5. Water body extraction from high spatial resolution remote sensing images based on enhanced U-Net and multi-scale information fusion H. Cao et al. https://doi.org/10.1038/s41598-024-67113-7
6. A Chlorophyll Concentration Inversion Method Based on OWTs and 1D CNN-Transformer Feature Extraction Y. Cui et al. https://doi.org/10.1109/JSTARS.2025.3641630