SATELLITE-DERIVED BATHYMETRY USING CONVOLUTIONAL NEURAL NETWORKS AND MULTISPECTRAL SENTINEL-2 IMAGES

8 citations as recorded by crossref.

1. Seabed Depth Prediction Using Multi-Scale Gravity Anomalies and Fully Connected Deep Neural Networks: A Novel Approach Applied to the South China Sea J. Yuan et al. 10.3390/rs17030412
2. Predicting beach profiles with machine learning from offshore wave reflection spectra E. Disdier et al. 10.1016/j.envsoft.2024.106221
3. Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks Y. Lumban-Gaol et al. 10.1080/01490419.2022.2091696
4. Incorporation of neighborhood information improves performance of SDB models A. Knudby & G. Richardson 10.1016/j.rsase.2023.101033
5. MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery X. Qin et al. 10.1016/j.isprsjprs.2024.09.007
6. Satellite derived bathymetry using empirical and machine learning approaches: a case study in the highly dynamic coastal water N. Hao Quang et al. 10.1080/21664250.2024.2445418
7. A Review of Image- and LiDAR-Based Mapping of Shallow Water Scenarios P. Kujawa & F. Remondino 10.3390/rs17122086
8. Lyzenga Algorithm for Shallow Water Mapping Using Multispectral Sentinel-2 Imageries in Gili Noko Waters D. Pratomo et al. 10.1051/bioconf/20248907006