Vol. 29, issue 07, article # 4

Abstract:

The method is considered for updating of the coastal water depths according to multi- and hyperspectral remote sensing imagery in the visible and near infrared spectral range. The depth recovery for each element of the image is produced on the basis of solving the inverse problem, which consists in training an artificial neural network using a semi-analytical model of radiation transfer in water taking into account the effects of scattering and absorption of light in the underwater light field at least in three informative spectral channels for each bottom type. At the same time, it is possible to adjust the learning process by means of regression algorithms for determination of organic and mineral impurities in water on their in-situ measurements. We enriched the library of the spectral characteristics of different types of bottom and found informative signs identifying them. The results are tested according to aviation and hyperspectral space imageries.

Keywords:

bathymetry, hyperspectral data, reflectance, absorption and scattering of light in water

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