Potential of Using Machine Learning Regression Techniques to Utilize Sentinel Images for Bathymetry Mapping of Nile River

Author:

Kamal NohaORCID,El-Ashmawy NagwaORCID

Publisher

Elsevier BV

Subject

General Earth and Planetary Sciences

Reference38 articles.

1. Remote bathymetry of the littoral zone from AVIRIS, LASH and QuickBird imagery;Adler-Golden;IEEE Transactions on Geoscience and Remote Sensing,2005

2. Analysis of univariate linear, robust-linear, and non-linear machine learning algorithms for satellite-derived bathymetry in complex coastal terrain;Ashphaq;Regional Studies in Marine Science,2022

3. Evaluation and performance of satellite-derived bathymetry algorithms in turbid coastal water: a case study of Vengurla rocks;Ashphaq;Indian Journal of Geo-Marine Sciences (IJMS),2022

4. A Physics Based Retrieval and Quality Assessment of Bathymetry from Suboptimal Hyperspectral Data;Brando;Remote Sensing of Environment,2009

5. Brisson, L., Wolfe, D. A. and Staley, M., 2014. Interferometric Swath Bathymetry for Large Scale Shallow Water Hydrographic Surveys Canadian Hydrographic Conference. 1 – 18.

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