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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 7, pp. 20-28

Texture segmentation of Earth’s surface noisy images

E.V. Medvedeva 1 , E.E. Kurbatova 1 , A.A. Okulova 1 
1 Vyatka State University, Kirov, Russia
Accepted: 20.11.2017
DOI: 10.21046/2070-7401-2017-14-7-20-28
A method for the detection of extended texture areas with homogeneous statistical characteristics on aerospace images distorted by additive white Gaussian noise is proposed. The method allows recovering digital images at low signal-to-noise ratio at the first stage and detecting texture areas at the second stage. The method is based on the representation of g-bit digital images by the set of g bit binary images that can be represented as random Markov process. We propose to use three-dimensional nonlinear filtering algorithm for noisy images preprocessing. The algorithm efficiently uses image statistical redundancy and allows obtaining more accurate estimates of image elements states. Binary images of high bits have the most pronounced texture features, because of it we propose to use them for texture areas detection. The estimates of the transition probability between binary image elements are used as the texture features. The sliding window has been used to calculate the statistical characteristics. Detection of regions with different textures is based on the analysis of texture feature histogram. The results of texture detection on artificial and real noisy aerospace images are shown. The texture segmentation quality is estimated by the number of erroneously segmented elements. The proposed method allows to divide noisy image to texture regions (with signal-to-noise ratio -6 dB) efficiently, if the transition probability between elements in the areas does not exceed 0.2. In this case the segmentation error is less than 8%.
Keywords: digital images, random Markov processes, texture segmentation, nonlinear filtering
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References:

  1. Gonsales R., Vuds R., Tsifrovaya obrabotka izobrazhenii (Digital Image Processing), Moscow: Tekhnosfera, 2012, 1104 p.
  2. Medvedeva E.V., Trubin I.S., Ustyuzhanina E.A., Laletin A.V., Nelineinaya mnogomernaya filtrasiya mnogokomponentnykh izobrazhenii (Multidimensional nonlinear filtration of multicomponent images), Mashinnoe obuchenie i analiz dannykh, 2015, Vol. 1, No. 13, pp. 1786–1795.
  3. http://m.progorodsamara.ru//news/view/167006.
  4. Fralenko V.P., Metody teksturnogo analiza izobrazhenii, obrabotka dannykh distantsionnogo zondirovaniya Zemli (Methods of image texture analysis, Earth remote sensing data processing), Programmnye sistemy: teoriya i prilozheniya, 2014, Vol. 5, No. 4, pp. 19–39.
  5. Shapiro L.G., Stokman D., Komp’yuternoe zrenie (Computer Vision), Moscow: BINOM, Laboratoriya znanii, 2006, 752 p.
  6. Shovengerdt R.A., Distantsionnoe zondirovanie. Modeli i metody obrabotki izobrazhenii (Remote Sensing. Models and Methods for image processing), Moscow: Tekhnosfera, 2010, 594 p.
  7. Haralick R.M., Statistical and structural approaches to texture, Proceedings of the IEEE, 1979, Vol. 67, No. 5, pp. 786–804.
  8. Kurbatova E.E., Medvedeva E.V., Okulova A.A., Method of isolating texture areas in images, Pattern Recognition and Image Analysis, 2015, Vol. 25, No. 1, pp. 47–52.
  9. Medvedeva E.V., Kurbatova E.E., A Two-stage image preprocessing algorithm, Pattern Recognition and Image Analysis, 2011, Vol. 21, No. 2, pp. 297–301.
  10. Petrov E.P, Trubin I.S., Medvedeva E.V., Smolskiy S.M., Mathematical Models of Video-Sequences of Digital Half-Tone Images, Integrated models for information communication systems and net-works: design and development, 2013, pp. 207–241.
  11. Petrov E.P, Trubin I.S., Medvedeva E.V., Smolskiy S.M., Development of Nonlinear Filtering Algorithms of Digital Half-Tone Images, Integrated models for information communication systems and net-works: design and development, 2013, pp. 278–304.
  12. Li S.Z., Markov Random Field Modeling in Image Analysis, Springer-Verlag London Limited, 2009, 569 p.
  13. Zhang J., Tan T., Brief review of invariant texture analysis methods, Pattern Recognition, 2002, Vol. 35 (3), No. 3, pp. 735–747.