COMPARATIVE INVESTIGATION OF NEURAL AND LOCALLY BINARY ALGORITHMS FOR IMAGE IDENTIFICATION OF PLANKTON POPULATIONS
https://doi.org/10.23947/2587-8999-2022-1-2-70-80
Abstract
The work is devoted to the «neural network-lbp» method of processing satellite images of multispectral water coastal systems for identification of phytoplankton populations of spotted structure: determination of their boundaries, distribution of color gradations and, based on this, determination of the distribution of phytoplankton concentrations inside the spots and the location of the center of mass. The efficiency and effectiveness of the proposed neural network-lbp method is investigated in comparison with the method based on the use of a three-layer neural network. For the analysis, a test set of images is involved – flat shapes with rather complex boundaries, which will allow us to quantify the quality of the algorithms being compared. The results of the work show an increase in recognition accuracy by 1.5-3% when using the proposed method.
Keywords
mathematical modeling,
local binary patterns,
neural network,
shallow water reservoir,
satellite sensing data
Supporting agencies: The research was supported by the Russian Science Foundation (project 20-01-00421)
About the Authors
A. I. Sukhinov
Don State Technical University
Russian Federation
Russian Federation
Sukhinov Alexander I., corresponding Member of the Russian Academy of Sciences, Doctor of Science in Physics and Maths, Professor
1st Gagarin Square, Rostov-on-Don
N. D. Panasenko
Don State Technical University
Russian Federation
Russian Federation
Panasenko Natalia D.
1st Gagarin Square, Rostov-on-Don
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Review
For citations:
Sukhinov A.I., Panasenko N.D. COMPARATIVE INVESTIGATION OF NEURAL AND LOCALLY BINARY ALGORITHMS FOR IMAGE IDENTIFICATION OF PLANKTON POPULATIONS. Computational Mathematics and Information Technologies. 2022;6(2):70-80. https://doi.org/10.23947/2587-8999-2022-1-2-70-80
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