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

Abstract

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