Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.259759

Keywords:

optoelectronic image, camouflaged military equipment, genetic algorithm, chromosome population

Abstract

The object of this research is the process of segmentation of camouflaged military equipment in images from space surveillance systems.

The method of segmentation of camouflaged military equipment in images from space surveillance systems has been improved using a genetic algorithm. Unlike known methods, the method of segmentation of camouflaged military equipment using a genetic algorithm involves the following:

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

– the use of a genetic algorithm in the image in each channel of brightness of the RGB color space;

– image segmentation is reduced to the formation of generations and populations of chromosomes, the calculation of the objective function, selection, crossing, mutation, and decoding of chromosomes in each brightness channel of the Red-Green-Blue color space.

Experimental studies were conducted on the segmentation of camouflaged military equipment using a genetic algorithm. It is established that the improved method of segmentation using a genetic algorithm makes it possible to segment images from space surveillance systems.

A comparison of the quality of segmentation was carried out. It is established that the improved method of segmentation using a genetic algorithm reduces segmentation errors in the following way:

– compared to the known k-means method, by an average of 15 % of errors of the first kind and an average of 7 % of errors of the second kind;

– compared to the method of segmentation based on the algorithm of swarm of particles, by an average of 3.8 % of errors of the first kind and an average of 2.9 % of errors of the second kind.

The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems

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

Ihor Butko, Academician Yuriy Bugay International Scientific and Technical University

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Igor Gyrenko, Institute of Special Communications and Information Protection of the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

PhD, Deputy Head of the Institute

Vitalii Stryhun, State Scientific Research Institute of Armament and Military Equipment Testing and Certification

Senior Researcher

Oleh Bilous, State Scientific Research Institute of Armament and Military Equipment Testing and Certification

Researcher

Nazar Shamrai, Ivan Kozhedub Kharkiv National Air Force University

Head of the Training Complex Group

Department of Tactics and Military Disciplines

Anna Kovalenko, Central Ukrainian National Technical University

PhD, Associate Professor

Department of Cybersecurity and Software

Irina Khizhnyak, Ivan Kozhedub Kharkiv National Air Force University

PhD

Department of Radar Troops Tactic

Rostyslav Khudov, V. N. Karazin Kharkiv National University

Department of Theoretical and Applied Informatics

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Published

2022-06-30

How to Cite

Khudov, H., Makoveichuk, O., Butko, I., Gyrenko, I., Stryhun, V., Bilous, O., Shamrai, N., Kovalenko, A., Khizhnyak, I., & Khudov, R. (2022). Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm . Eastern-European Journal of Enterprise Technologies, 3(9 (117), 6–14. https://doi.org/10.15587/1729-4061.2022.259759

Issue

Vol. 3 No. 9 (117) (2022): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2022 Hennadii Khudov, Oleksandr Makoveichuk, Ihor Butko, Igor Gyrenko, Vitalii Stryhun, Oleh Bilous, Nazar Shamrai, Anna Kovalenko, Irina Khizhnyak, Rostyslav Khudov

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