Segmentation of optical-electronic images from on-board systems of remote sensing of the earth by the artificial bee colony method

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.161860

Keywords:

remote sensing of the Earth, image, segmentation, artificial bee colony method

Abstract

It was established that it is not possible to apply the known methods of image segmentation directly to segmentation of optical-electronic images of on-board systems of remote sensing of the Earth. We have stated the mathematical problem on segmentation of such images. It was established that the result of segmentation of images of on-board systems of remote sensing of the Earth is separation of an image into artificial objects (objects of interest) and natural objects (a background). It has been proposed to use the artificial bee colony method for segmentation of images. We described the essence of the method, which provides for determination of agents positions, their migration, conditions for stopping of an iteration process by the criterion of a minimum of a fitness function and determination of the optimal value of a threshold level. The fitness function was introduced, which has the physical meaning of a sum of variance brightness of segments of a segmented image. We formulated the optimization problem of image segmentation of an on-board optical-electronic observation system. It consists in minimization of a fitness function under certain assumptions and constraints.

The paper presents results from an experimental study on application of the artificial bee colony method to segmentation of an optical-electronic image. Experimental studies on segmentation of an optical-electronic image confirmed the efficiency of the artificial bee colony method. We identified possible objects of interest on the segmented image, such as tanks with oil or fuel for aircraft, airplanes, airfield facilities, etc.

The visual assessment of the quality of segmentation was performed. We calculated errors of the first type and the second type. It was established that application of the artificial bee colony method would improve the quality of processing of optical-electronic images. We observed a decrease of segmentation errors of the first type and the second type by the magnitude from 7 % to 33 % on average

Author Biographies

Igor Ruban, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

Doctor of Technical Sciences, Professor, First Vice-Rector

Hennadii Khudov, Ivan Kozhedub Kharkiv University of Air Force Sumska str., 77/79, Kharkiv, Ukraine, 61023

Doctor of Technical Sciences, Professor, Head of Department

Department of Radar Troops Tactic

Oleksandr Makoveichuk, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

PhD

Department of Electronic Computers

Irina Khizhnyak, Ivan Kozhedub Kharkiv University of Air Force Sumska str., 77/79, Kharkiv, Ukraine, 61023

PhD, Lecturer

Department of Mathematical and Software Automated Control Systems

Vladyslav Khudov, Kharkiv National University of Radio Electronics Nauky ave., 14, Kharkiv, Ukraine, 61166

Junior Researcher

Department of Information Technology Security

Viacheslav Podlipaiev, Institute of Telecommunications and Global Information Space Chokolivskiy blvd., 13, Kyiv, Ukraine, 03186

PhD, Researcher

Department of Information and Communication Technologies

Viktor Shumeiko, Institute of Telecommunications and Global Information Space Chokolivskiy blvd., 13, Kyiv, Ukraine, 03186

PhD, Researcher

Department of Information and Communication Technologies

Oleksandr Atrasevych, Ivan Chernyakhovsky National Defense University of Ukraine Povitroflotskyi ave., 28, Kyiv, Ukraine, 03049

Department of Space Systems Applianceand Geoinformation Support

Anatolii Nikitin, Ivan Chernyakhovsky National Defense University of Ukraine Povitroflotskyi ave., 28, Kyiv, Ukraine, 03049

Adjunct

Rostyslav Khudov, V. N. Karazin Kharkiv National University Svobody sq., 4, Kharkiv, Ukraine, 61022

Department of Theoretical and Applied Informatics

References

  1. Chemin, Y. (Ed.) (2012). Remote Sensing of Planet Earth. Rijeka. doi: https://doi.org/10.5772/2291
  2. Gonzalez, R., Woods, R. E. (2002). Digital Image Processing. Prentice Hall.
  3. Ruban, I., Khudov, H., Khudov, V., Khizhnyak, I., Makoveichuk, O. (2017). Segmentation of the images obtained from onboard optoelectronic surveillance systems by the evolutionary method. Eastern-European Journal of Enterprise Technologies, 5 (9 (89)), 49–57. doi: https://doi.org/10.15587/1729-4061.2017.109904
  4. Dhanachandra, N., Manglem, K., Chanu, Y. J. (2015). Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm. Procedia Computer Science, 54, 764–771. doi: https://doi.org/10.1016/j.procs.2015.06.090
  5. Makkar, H., Pundir, A. (2014). Image Analysis Using Improved Otsu’s Thresholding Method. International Journal on Recent and Innovation Trends in Computing and Communication, 2 (8), 2122–2126.
  6. Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8 (6), 679–698. doi: https://doi.org/10.1109/tpami.1986.4767851
  7. Faroogue, M. Y., Raeen, M. S. (2014). Latest trends on image segmentation schemes. International journal of advanced research in computer science and software engineering, 4 (10), 792–795.
  8. El-Baz, A., Jiang, X., Jasjit, S. (Eds.) (2016). Biomedical image segmentation: advances and trends. CRC Press, 546. doi: https://doi.org/10.4324/9781315372273
  9. Choudhary, R., Gupta, R. (2017). Recent Trends and Techniques in Image Enhancement using Differential Evolution- A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 7 (4), 106–112. doi: https://doi.org/10.23956/ijarcsse/v7i4/0108
  10. Wang, Y. (2014). A New Image Threshold Segmentation based on Fuzzy Entropy and Improved Intelligent Optimization Algorithm. Journal of Multimedia, 9 (4). doi: https://doi.org/10.4304/jmm.9.4.499-505
  11. Dey, V., Zhang, Y., Zhong, M. (2010). A review on image segmentation techniques with remote sensing perspective. Proceedings ISPRS TC VII Symposium, IAPRS, XXXVII, 31–42.
  12. Michel, J., Youssefi, D., Grizonnet, M. (2015). Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 53 (2), 952–964. doi: https://doi.org/10.1109/tgrs.2014.2330857
  13. Tasdemir, K., Moazzen, Y., Yildirim, I. (2015). An Approximate Spectral Clustering Ensemble for High Spatial Resolution Remote-Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (5), 1996–2004. doi: https://doi.org/10.1109/jstars.2015.2424292
  14. Hachouf, F., Zeggari, A. (2005). Genetic optimization for unsupervised fuzzy classification. 17 Congres Mondial IMACS, 27–32.
  15. Choi, T.-M., Kim, S. Y. (2005). Fuzzy Types Clustering for Microarray Data. Proceedings of world academy of science, engineering and technology, 4, 12–15.
  16. OpenCV library. Available at: https://opencv.org
  17. Peredovye tekhnologii v obrabotke DDZ. Available at: http://www.mapinfo.ru/product/erdas
  18. Paket ArcView. Sistema ArcInfo. Available at: http://geoknigi.com/book_view.php?id=629
  19. Scanex – lider v sfere sputnikovogo monitoringa. Available at: http://scanex.ru
  20. TNTmips. Available at: http://www.microimages.com/products/tntmips.htm
  21. Ruban, I., Khudov, V., Khudov, H., Khizhnyak, I. (2017). An improved method for segmentation of a multiscale sequence of optoelectronic images. 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2017.8246367
  22. Ruban, I., Khudov, V., Makoveichuk, O., Khudov, H., Khizhnyak, I. (2018). A Swarm Method for Segmentation of Images Obtained from On-Board Optoelectronic Surveillance Systems. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T). doi: https://doi.org/10.1109/infocommst.2018.8632045
  23. Bolaji, A. L., Khader, A. T., Al-Betar, M. A., Awadallah, M. A. (2013). Artificial bee colony algorithm, its variants and applications: a survey. Journal of Theoretical and Applied Information Technology, 47 (2), 434–459.
  24. Kumar, A., Kumar, D., Jarial, S. K. (2017). A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering. Cybernetics and Information Technologies, 17 (3), 3–28. doi: https://doi.org/10.1515/cait-2017-0027
  25. Karaboga, D., Akay, B. (2009). A survey: algorithms simulating bee swarm intelligence. Artificial Intelligence Review, 31 (1-4), 61–85. doi: https://doi.org/10.1007/s10462-009-9127-4
  26. Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N. (2012). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42 (1), 21–57. doi: https://doi.org/10.1007/s10462-012-9328-0
  27. Bansal, J. C., Sharma, H., Jadon, S. S. (2013). Artificial bee colony algorithm: a survey. International Journal of Advanced Intelligence Paradigms, 5 (1/2), 123. doi: https://doi.org/10.1504/ijaip.2013.054681
  28. Balasubramani, K., Marcus, K. (2006). A Comprehensive review of Artificial Bee Colony Algorithm. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 5 (1), 15–28. doi: https://doi.org/10.24297/ijct.v5i1.4382

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Published

2019-04-02

How to Cite

Ruban, I., Khudov, H., Makoveichuk, O., Khizhnyak, I., Khudov, V., Podlipaiev, V., Shumeiko, V., Atrasevych, O., Nikitin, A., & Khudov, R. (2019). Segmentation of optical-electronic images from on-board systems of remote sensing of the earth by the artificial bee colony method. Eastern-European Journal of Enterprise Technologies, 2(9 (98), 37–45. https://doi.org/10.15587/1729-4061.2019.161860

Issue

Vol. 2 No. 9 (98) (2019): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2019 Igor Ruban, Hennadii Khudov, Oleksandr Makoveichuk, Irina Khizhnyak, Vladyslav Khudov, Viacheslav Podlipaiev, Viktor Shumeiko, Oleksandr Atrasevych, Anatolii Nikitin, Rostyslav Khudov

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