Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.255203

Keywords:

segmentation, complex structured image, space surveillance system, particle swarm, errors of the first and second kind

Abstract

This paper considers the improved method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Unlike known ones, the method for segmenting complex structured images based on the particle swarm algorithm involves the following:

– highlighting brightness channels in the Red-Green-Blue color space;

– using a particle swarm method in the image in each channel of brightness of the RGB color space;

– image segmentation is reduced to calculating the objective function, moving speed, and a new location for each swarm particle in the image in each RGB color space brightness channel.

Experimental studies have been conducted on the segmentation of a complex structured image by a method based on the particle swarm algorithm. It was established that the improved segmentation method based on the particle swarm algorithm makes it possible to segment complex structured images acquired from space surveillance systems.

A comparison of the quality of segmenting a complex structured image was carried out. The comparative visual analysis of well-known and improved segmentation methods indicates the following:

– the improved segmentation method based on the particle swarm algorithm highlights more objects of interest (objects of military equipment);

– the well-known k-means method assigns some objects of interest (especially those partially covered with snow) to the snow cover (marked in blue);

– the improved segmentation method also associates some objects of interest that are almost completely covered with snow with the snow cover (marked in blue).

It has been established that the improved segmentation method based on the particle swarm algorithm reduces segmentation errors of the first kind by an average of 12 % and reduces segmentation errors of the second kind by an average of 8 %

Author Biographies

Hennadii Khudov, Ivan Kozhedub Kharkiv National Air Force University

Doctor of Technical Sciences, Professor, Head of Department

Department of Radar Troops Tactic

Oleksandr Makoveichuk, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences, Associate Professor

Department of Electronic Computers

Irina Khizhnyak, Ivan Kozhedub Kharkiv National Air Force University

PhD

Department of Radar Troops Tactic

Oleksandr Oleksenko, Ivan Kozhedub Kharkiv National Air Force University

Adjunct

Department of Radar Troops Tactic

Yuriy Khazhanets, The National Defence University of Ukraine named after Ivan Cherniakhovskyi

Adjunct

Department of Aviation and Air Defence

Yuriy Solomonenko, Ivan Kozhedub Kharkiv National Air Force University

PhD

Department of Radar Troops Tactic

Iryna Yuzova, Civil Aviation Institute

PhD, Lecturer

Department of Information Technologies

Yevhen Dudar, Hetman Petro Sahaidachnyi National Army Academy

Deputy Head of Department

Department of Troop Training

Stanislav Stetsiv, Hetman Petro Sahaidachnyi National Army Academy

Senior Lecturer

Department of Missile Forces

Vladyslav Khudov, Kharkiv National University of Radio Electronics

PhD, Junior Researcher

Department of Information Technology Security

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Published

2022-04-30

How to Cite

Khudov, H., Makoveichuk, O., Khizhnyak, I. ., Oleksenko, O., Khazhanets, Y., Solomonenko, Y., Yuzova, I., Dudar, Y., Stetsiv, S., & Khudov, V. (2022). Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm . Eastern-European Journal of Enterprise Technologies, 2(9 (116), 6–13. https://doi.org/10.15587/1729-4061.2022.255203

Issue

Vol. 2 No. 9 (116) (2022): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2022 Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Oleksandr Oleksenko, Yuriy Khazhanets, Yuriy Solomonenko, Iryna Yuzova, Yevhen Dudar, Stanislav Stetsiv, Vladyslav Khudov

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