DEEP LEARNING APPLIED TO WATER SEGMENTATION

13 citations as recorded by crossref.

1. Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
2. Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data O. Andrew et al. 10.3390/ijgi12050194
3. Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia E. Ansari et al. 10.3390/su13179568
4. Integration of object detection and semantic segmentation based on convolutional neural networks for navigation and monitoring of cyanobacterial blooms in lentic water scenes F. Barrientos-Espillco et al. 10.1016/j.asoc.2024.111849
5. Efficient Water Segmentation with Transformer and Knowledge Distillation for USVs J. Zhang et al. 10.3390/jmse11050901
6. A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery A. Moghimi et al. 10.1109/ACCESS.2024.3385425
7. Automatic Segmentation of Water Bodies Using RGB Data: A Physically Based Approach M. García et al. 10.3390/rs15051170
8. Deep learning and LiDAR integration for surveillance camera-based river water level monitoring in flood applications N. Muhadi et al. 10.1007/s11069-024-06503-6
9. Open Water Detection for Autonomous In-harbor Navigation Using a Classification Network M. Plenge-Feidenhans’l & M. Blanke 10.1016/j.ifacol.2021.10.069
10. Fast Segmentation Algorithm of USV Accessible Area Based on Attention Fast Deeplabv3 L. Cheng et al. 10.1109/JSEN.2024.3410403
11. Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices Y. Lee et al. 10.3390/rs16132358
12. Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
13. Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks M. Irie et al. 10.3390/rs16010173