Algorithms and programs based on neural networks and local binary patterns approaches for monitoring plankton populations in sea systems

The work is devoted to the method of multispectral space images analyzing aquatic coastal systems for identifying phytoplankton populations complicated structures: determining their boundaries, distributing color gradations and, based on this, distribution concentrations within patches and location "mass" center. A combination local binary patterns (LBP) neuralnetworks methods considered. Due these characteristics it possible, basing a series processed same water area different time points (dates), determine changing rate in spots boundaries concentrations, shift mass center which are influenced by environment movement processes growth death. results allow us Azov Sea state. Experimental data program given confirmation part.