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Prof. Mohamed Ibrahim Zahran :: Publications:

Title:
Assessment of artificial neural network for bathymetry estimation using High resolution satellite imagery in shallow Lakes: Case study El Burullus lake.
Authors: Hassan Mohamed, Abdelazim Negm, Mohamed Zahran, and Oliver C. Saavedra.
Year: 2015
Keywords: Not Available
Journal: International Water Technology Journal IWTJ
Volume: 5
Issue: 4
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper mohamed ibrahim zahran_puplished paper-1 journal.pdf
Supplementary materials Not Available
Abstract:

In this paper, a new method for estimating shallow- water depths (bathymetric map) from multispectral images is proposed. This method is based on using Artificial Neural Network fitting algorithms using reflectance of bands influencing water depths and their logarithms for bathymetry detection. An automated method for calibrating the parameters for a Log- Nonlinear inversion model was developed using Levenberg-Marquardt training algorithm. The ANN fitting algorithms using Green and Red bands reflectance and their logarithms was compared with ANN using only Green band reflectance, four SPOT-4 image bands reflectance, and two conventional models (Third order polynomial correlation using the Green band Reflectance and Generalized Linear Model using both Green and Red bands reflectance).

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