A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY

36 citations as recorded by crossref.

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21. Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks Y. Lumban-Gaol et al.
22. Improving the Accuracy of Satellite-Derived Bathymetry Using Multi-Layer Perceptron and Random Forest Regression Methods: A Case Study of Tavşan Island O. Çelik et al.
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28. Evaluation of Satellite-Derived Bathymetry from High and Medium-Resolution Sensors Using Empirical Methods E. Evagorou et al.
29. Application of Sentinel-2 Imagery and Machine Learning for Predicting Coastal Bathymetry in the Cisadane Estuary, Indonesia H. Aziz et al.
30. Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain) V. Mateo-Pérez et al.
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33. Shallow Water Depth Estimation of Inland Wetlands Using Landsat 8 Satellite Images C. Owusu et al.
34. Satellite-Derived Bathymetry in Support of Maritime Archaeological Research—VENμS Imagery of Caesarea Maritima, Israel, as a Case Study G. Diaz et al.
35. Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2 X. Zhang et al.
36. SaTSeaD: Satellite Triangulated Sea Depth Open-Source Bathymetry Module for NASA Ames Stereo Pipeline M. Palaseanu-Lovejoy et al.