The retrieval of the coastal water depths from data of multi- and hyperspectral remote sensing imagery

Resumo

A method is considered for rendering coastal water depths according to multi- and hyperspectral remote sensing imagery in the visible and near-infrared spectral regions. The depth is recovered for each pixel on the basis of solution of the inverse problem, which consists in artificial neural network learning with the use of a semianalytical model of radiation transfer in water, taking into account the effects of light scattering and absorption in the underwater light field, at least in three informative spectral channels for each bottom type. A possibility of adjusting the learning process is provided by the use of regression algorithms for determining organic and mineral impurities in water from their in-situ measurements. We enriched the library of the spectral characteristics of different bottom types and found informative identifiers for them. The results are tested on aircraft and hyperspectral space imagery data.