Improving a method for filtering images acquired from a space-based radar observation system based on the Kuan algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.352347

Keywords:

space radar surveillance system, image filtering, Kuan algorithm, signal-to-noise ratio

Abstract

The object of this study is the process of filtering images acquired from a space radar observation system. The task to filter images from a space radar observation system has been solved by applying the Kuan algorithm.

The results reported here include the following:

– the defined basic stages of the method for filtering images acquired from a space radar observation system based on the Kuan algorithm;

– the performed experimental study on filtering images from a space radar observation system based on the Kuan algorithm.

A method for filtering images from a space radar observation system based on the Kuan algorithm has been improved. Special features of the improved method, in contrast to those in established ones, are:

– selection of a local filtering window;

– calculation of local statistical characteristics;

– calculation of variation coefficients;

– calculation of the Kuan weight coefficient;

– sequential filtering of image pixels using the “sliding” window method.

A visual analysis of radar image filtering by an improved method based on the Kuan algorithm and known methods based on the Li algorithm and Frost algorithm were carried out. The use of the improved method when filtering an image acquired from a space radar surveillance system made it possible to increase the signal-to-noise ratio. That became possible due to the use of the Kuan algorithm. The choice of the Kuan algorithm has made it possible to achieve a 21% gain in the maximum signal-to-noise ratio in comparison with the known method (based on the Li algorithm).

The scope of the improved method application includes filtering images from space radar surveillance systems. Conditions for practical implementation of the results are specialized software in software-hardware systems for processing radar images

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, Academician Yury Bugai International Scientific and Technical University Ukraine

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Serhii Tokarev, Ivan Kozhedub Kharkiv National Air Force University

Lecturer

Department of Engineering and Aviation Support

Artem Andriushchenko, Ivan Kozhedub Kharkiv National Air Force University

PhD Student

Oleksandr Pukhovyi, National Defence University of Ukraine

PhD, Associate Professor, Head of Department

Department of Radio-Technical and Special Troops

Olexandr Rohulia, Ivan Kozhedub Kharkiv National Air Force University

Leading Researcher

Research Department of Air Force Science Center

Oleh Bilous, National Defence University of Ukraine

PhD Student

Mykola Verovok, National Defence University of Ukraine

PhD Student

National Defence University of Ukraine

Valeriy Samoylenko, National Academy of the National Guard of Ukraine

PhD, Associate Professor

Department of Fire Training

Vladyslav Khudov, Kharkiv National University of Radio Electronics

PhD, Junior Researcher

Department of Information Technology Security

References

  1. Zhang, M., Ouyang, Y., Yang, M., Guo, J., Li, Y. (2025). ORPSD: Outer Rectangular Projection-Based Representation for Oriented Ship Detection in SAR Images. Remote Sensing, 17 (9), 1511. https://doi.org/10.3390/rs17091511
  2. Amitrano, D., Di Martino, G., Di Simone, A., Imperatore, P. (2024). Flood Detection with SAR: A Review of Techniques and Datasets. Remote Sensing, 16 (4), 656. https://doi.org/10.3390/rs16040656
  3. Hrushko, O., Zhytar, D., Ilkiv, E., Hrynishak, M., Kukhtar, D. (2025). Geospatial Analysis of War-Affected Areas in Ukraine Based on SAR and GIS Technologies. 18th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, 1–5. https://doi.org/10.3997/2214-4609.2025510159
  4. Pavlikov, V., Zhyla, S., Pozdniakov, P., Kolesnikov, D., Cherepnin, H., Shmatko, O. et al. (2024). Foundations of radar synthesis theory of phantom objects formation in SAR images. Radioelectronic and Computer Systems, 2024 (4), 123–140. https://doi.org/10.32620/reks.2024.4.11
  5. Kostenko, P. Yu., Slobodyanuk, V. V., Plahotenko, O. V. (2016). Method of image filtering using singular decomposition and the surrogate data technology. Radioelectronics and Communications Systems, 59 (9), 409–416. https://doi.org/10.3103/s0735272716090041
  6. Kostenko, P. Yu., Slobodyanuk, V. V., Kostenko, I. L. (2019). Method of Image Denoising in Generalized Phase Space with Improved Indicator of Spatial Resolution. Radioelectronics and Communications Systems, 62 (7), 368–375. https://doi.org/10.3103/s0735272719070045
  7. Kryvenko, S., Lukin, V., Vozel, B. (2024). Lossy Compression of Single-channel Noisy Images by Modern Coders. Remote Sensing, 16 (12), 2093. https://doi.org/10.3390/rs16122093
  8. Kryvenko, S., Rebrov, V., Lukin, V., Golovko, V., Sachenko, A., Shelestov, A., Vozel, B. (2025). Post-Filtering of Noisy Images Compressed by HEIF. Applied Sciences, 15 (6), 2939. https://doi.org/10.3390/app15062939
  9. Al-Bayati, M., El-Zaart, A. (2013). Automatic Thresholding Techniques for SAR Images. Computer Science & Information Technology (CS&IT), 4 (3), 75–84. https://doi.org/10.5121/csit.2013.3308
  10. Tan, J., Tang, Y., Liu, B., Zhao, G., Mu, Y., Sun, M., Wang, B. (2023). A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block. Remote Sensing, 15 (10), 2690. https://doi.org/10.3390/rs15102690
  11. Hillebrand, F. L., Prieto, J. D., Mendes Júnior, C. W., Arigony-Neto, J., Simões, J. C. (2024). Gray Level Co-occurrence Matrix textural analysis for temporal mapping of sea ice in Sentinel-1A SAR images. Anais Da Academia Brasileira de Ciências, 96 (2). https://doi.org/10.1590/0001-3765202420240554
  12. Zhai, Y., Liu, K., Piuri, V., Ying, Z., Xu, Y. (2016). SAR automatic target recognition based on K-means and data augmentation. Proceedings of the 2016 International Conference on Intelligent Information Processing, 1–6. https://doi.org/10.1145/3028842.3028894
  13. Chen, Z., Cong, B., Hua, Z., Cengiz, K., Shabaz, M. (2021). Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation. Journal of Intelligent Systems, 30 (1), 1014–1025. https://doi.org/10.1515/jisys-2021-0096
  14. Salehi, H., Vahidi, J., Abdeljawad, T., Khan, A., Rad, S. Y. B. (2020). A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter. Remote Sensing, 12 (15), 2371. https://doi.org/10.3390/rs12152371
  15. Rubel, O., Lukin, V., Rubel, A., Egiazarian, K. (2021). Selection of Lee Filter Window Size Based on Despeckling Efficiency Prediction for Sentinel SAR Images. Remote Sensing, 13 (10), 1887. https://doi.org/10.3390/rs13101887
  16. Ruban, I., Khudov, H., Makoveichuk, O., Khudov, V., Kalimulin, T., Glukhov, S. et al. (2022). Methods of UAVs images segmentation based on k-means and a genetic algorithm. Eastern-European Journal of Enterprise Technologies, 4 (9 (118)), 30–40. https://doi.org/10.15587/1729-4061.2022.263387
  17. Khudov, H., Makoveichuk, O., Khizhnyak, I., Shamrai, B., Glukhov, S., Lunov, O. et al. (2022). The Method for Determining Informative Zones on Images from On-Board Surveillance Systems. International Journal of Emerging Technology and Advanced Engineering, 12 (8), 61–69. https://doi.org/10.46338/ijetae0822_08
  18. Kanakaraj, S., Nair, M. S., Kalady, S. (2019). Adaptive Importance Sampling Unscented Kalman Filter based SAR image super resolution. Computers & Geosciences, 133, 104310. https://doi.org/10.1016/j.cageo.2019.104310
  19. Rubel, O. S., Rubel, A. S., Lukin, V., Egiazarian, K. (2022). Optimal parameters selection of the Frost filter based on despeckling efficiency prediction for Sentinel SAR images. Electronic Imaging, 34 (10), 193-1-193–196. https://doi.org/10.2352/ei.2022.34.10.ipas-193
  20. Ruban, I., Khudov, H., Khudov, V., Makoveichuk, O., Khizhnyak, I., Shamrai, N. et al. (2025). Development of an image segmentation method from unmanned aerial vehicles based on the ant colony algorithm under the influence of speckle noise. Technology Audit and Production Reserves, 4 (2 (84)), 80–86. https://doi.org/10.15587/2706-5448.2025.334993
  21. Benes, R., Riha, K. (2012). Medical Image Denoising by Improved Kuan Filter. Advances in Electrical and Electronic Engineering, 10 (1), 43–49. https://doi.org/10.15598/aeee.v10i1.529
  22. Tripathi, A., Bhateja, V., Sharma, A.; Mandal, J., Satapathy, S., Sanyal, M., Bhateja, V. (Eds.) (2016). Kuan Modified Anisotropic Diffusion Approach for Speckle Filtering. Proceedings of the First International Conference on Intelligent Computing and Communication. Singapore: Springer, 537–545. https://doi.org/10.1007/978-981-10-2035-3_55
  23. Sentinel-1. European Space Agency. Available at: https://sentinels.copernicus.eu/copernicus/sentinel-1
  24. Sun, Z., Zhang, Z., Chen, Y., Liu, S., Song, Y. (2020). Frost Filtering Algorithm of SAR Images With Adaptive Windowing and Adaptive Tuning Factor. IEEE Geoscience and Remote Sensing Letters, 17 (6), 1097–1101. https://doi.org/10.1109/lgrs.2019.2939208
Improving a method for filtering images acquired from a space-based radar observation system based on the Kuan algorithm

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Published

2026-02-27

How to Cite

Khudov, H., Makoveichuk, O., Tokarev, S., Andriushchenko, A., Pukhovyi, O., Rohulia, O., Bilous, O., Verovok, M., Samoylenko, V., & Khudov, V. (2026). Improving a method for filtering images acquired from a space-based radar observation system based on the Kuan algorithm. Eastern-European Journal of Enterprise Technologies, 1(9 (139), 40–46. https://doi.org/10.15587/1729-4061.2026.352347

Issue

Vol. 1 No. 9 (139) (2026): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2026 Hennadii Khudov, Oleksandr Makoveichuk, Serhii Tokarev, Artem Andriushchenko, Oleksandr Pukhovyi, Оlexandr Rohulia, Oleh Bilous, Mykola Verovok, Valeriy Samoylenko, Vladyslav Khudov

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