Comparative analysis of entropic metrics of space objects optical images informativity


DOI: 10.34759/trd-2020-112-10

Аuthors

Timoshenko A. V.1*, Koshkarov A. S.2**

1. Radiotechnical Institute named after academician A.L. Mintz, 10-1, str. 8 March, Moscow, 127083, Russia
2. Mlitary spaсe Aсademy named after A.F. Mozhaisky, Saint Petersburg, Russia

*e-mail: atimoshenko@rti-mintsu
**e-mail: vka@mil.ru

Abstract

The existing tendency to the intensity increasing of near-Earth space employing by ever-increasing number of states, expanding spectrum and increasing complexity level of the space operations, the expected deployment of the extra-large satellite systems for group space missions are increasing the risks for the space activities associated with technogenic contamination. Optical images are the most informative type of information on the situation in outer space. The article brings up issues concerning preliminary images rejection obtained by the ground-based optical means, unfit for the human operator recognition. Specifics of the space objects surveillance by the ground-based optical means with account for dangers for national space vehicles and manned flights safety are considered. Estimation of the conventional network of optical means is performed. The necessity of performing preliminary rejection of the images, being obtained, prior to the analysis performing by the human operator is substantiated. It is shown that the main post-detector processing method is blind deconvolution of images. The work with the functional of the entropy of the image being restored is required while deconvolution operations performing. Entropy metrics by Shannon, Tsallis, Capture and Rennie were selected for the study. A comparative analysis of the entropy metrics of informativity, which application is in demand when computing the deconvolution functional, was performed. Test images with different visual quality were selected. On the example of real images a possibility of their employing with account of images visual perception by the human operator was estimated. Within the framework of the conducted studies inferences were drawn on the applicability of employing various entropy metrics and the degree of their consistency with the structural-semantic approach of visual perception of images by a human operator. The trends of future studies on determining the generalized metric for the images of such class, as well as applicability of entropy metrics to the processing satellite-obtained data on the remote Earth monitoring were determined.

Keywords:

images analysis, space debris, images informativity, visual perception

References

  1. Veniaminov S.S. Kosmicheskii musor – ugroza chelovechestvu (Space debris is a threat to humanity), Moscow, Izd-vo IKI RAN, 2013, 312 p.

  2. Shustov B.M., Rykhlova L.V. Asteroidno-kometnaya opasnost’: vchera, segodnya, zavtra (Asteroid and comet danger: yesterday, today, tomorrow), Moscow, Fizmatlit, 2010, 384 p.

  3. Marr D. Zrenie. Informatsionnyi podkhod k izucheniyu predstavleniya i obrabotki zritel’nykh obrazov (Vision. Informational approach to studying visual images representation and processing), Moscow, Radio i svyaz’, 1987, 400 p.

  4. Aksenov A.Yu., Oleinikov I.I., Pokuchaev V.N., Pnyrin V.V. Polet, 2013, no. 10, pp. 11 – 16.

  5. Molotov I.E., Voropaev V.A., Yudin A.N. et al. Ekologicheskii vestnik nauchnykh tsentrov ChES, 2017, vol. 14, no. 4-2, pp. 110 – 116.

  6. Shilin V.D., Luk’yanov A.P., Molotov I.E., Agapov V.M. et al. Ekologicheskii vestnik nauchnykh tsentrov ChES, 2013, vol. 10, no. 4-2, pp. 171 – 175.

  7. Zinov’ev Yu.S., Mishina O.A., Glushchenko A.A. Trudy MAI, 2018, no. 101, available at: http://trudymai.ru/eng/published.php?ID=96976

  8. Kirichenko D.V., Kleimenov V.V., Novikova E.V. Izvestiya vuzov. Priborostroenie, 2017, vol. 60, no. 7, pp. 589 – 602.

  9. Bel’skii A.B., Zdor S.E., Kolin’ko V.I., Yatskevich N.G. Opticheskii zhurnal, 2009, no. 8, pp 22 – 28.

  10. Mal’tsev G.N. Opticheskii zhurnal, 2001, no. 10, pp. 65 – 69.

  11. Verba V.S. et al. Radiolokatsionnye sistemy zemleobzora kosmicheskogo bazirovaniya (Space-based ground survey radar systems), Moscow, Radiotekhnika, 2010, 680 p.

  12. Gonsales R., Vuds R., Eddins S. Tsifrovaya obrabotka izobrazhenii v srede MATLAB (Digital image processing in MATLAB environment), Moscow, Tekhnosfera, 2006, 616 p.

  13. Vasilenko G.I., Taratorin A.M. Vosstanovlenie izobrazhenii (Image Recovering), Moscow, Radio i svyaz’, 1986, 304 p.

  14. Chen Sh.-K. Printsipy proektirovaniya sistem vizual’noi informatsii (Principles of Visual Information Systems Design), Moscow, Mir, 1994, 408 p.

  15. Shigaev A.K., Luchkin R.S., Mashkin M.N., Tyurin V.S. Trudy MAI, 2015, no. 82, available at: http://trudymai.ru/eng/published.php?ID=58775

  16. Pantin E., Starck J.-L. Deconvolution of astronomical images using the multiscale maximum entropy method, Astronomy and Astrophysics Supplement Series, 1996, vol. 118, no. 3, pp. 575 – 585. DOI: https://doi.org/10.1051/aas:1996221

  17. Starck J.-L., Murtagh F., Querre P., Bonnarel F. Astronomy and Astrophysics, 2001, vol. 368, pp. 730 – 746. DOI: https://doi.org/10.1051/0004-6361:20000575

  18. N. Ageorges, C. Dainty. Laser Guide Star Adaptive Optics for Astronomy, Springer Science & Business Media, 2000, 340 p.

  19. Kazbekov B.V. Trudy MAI, 2013, no. 65, available at: http://trudymai.ru/eng/published.php?ID=35912

  20. Tsytsulin A.K., Adamov D.Yu., Mantsvetov A.A., Zubakin I.A. Tverdotel’nye telekamery: nakoplenie kachestva informatsii (Solid-state cameras: accumulation of information quality), Saint-Petersburg, Izd-vo LETI, 2014, 272 p.

  21. Chumak O.V. Entropii i fraktaly v analize dannykh (Entropy and fractals in data analysis), Moscow-Izhevsk, NITs “Regulyarnaya i khaoticheskaya dinamika”, Institut komp’yuternykh issledovanii, 2011, 164 p.

  22. Patni G.C., Jain K.C. Axiomatic Characterization of Some non-additive measures of Information, Metrika: International Journal for Theoretical and Applied Statistics, 1977, vol. 24, issue 1, pp. 23 – 34.

  23. Amelia Carolina Sparavigna. On the Role of Tsallis Entropy in Image Processing, International Scientific Research Journal, IRJ Science, 2015, no. 1 (6), pp.16 – 24.

  24. A. Ramírez, A. Raúl Hernández, G. Herrra, I. Domínguez. Determining the Entropic Index q of Tsallis Entropy in Images through Redundancy, Entropy, 2016, no. 18, pp. 299 – 313, available at: https://doi.org/10.3390/e18080299

  25. M.S.R. Naidua, P.Rajesh Kumar, K. Chiranjeevic. Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation, Alexandria Engineering Journal, 2018, vol. 57, issue 3, pp. 1643 – 1655. DOI: 10.1016/j.aej.2017.05.024


Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

 Вход