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Fusion Markov Random Field Image Segmentation for a Time Series of Remote Sensed Images

  • Tamás SzirányiEmail author
  • Andras Kriston
  • Andras Majdik
  • Laszlo Tizedes
Conference paper
Part of the Mathematics in Industry book series (MATHINDUSTRY, volume 30)

Abstract

Change detection on images of very different time instants from remote sensing databases and up-to-date satellite born or UAV born imaging is an emerging technology platform today. Since outdoor sceneries, principally observation of natural reserves, agricultural meadows and forest areas, are changing in illumination, coloring, textures and shadows time-by-time, and the resolution and geometrical properties of the imaging conditions may be also diverse, robust and semantic level algorithms should be developed for the comparison of images of the same or similar places in very different times. Earlier, a new method, fusion Markov Random Field (fMRF) method has been introduced which applied unsupervised or partly supervised clustering on a fused image series by using cross-layer similarity measure, followed by a multi-layer Markov Random Field segmentation. This paper shows the effective parametrization of the fusion MRF segmentation method for the analysis of agricultural areas of fine details and difficult subclasses.

Notes

Acknowledgements

The research reported in this paper was supported by Hungarian Scientific Research Fund (No. NKFIH OTKA K-120499 and KH-126513).

References

  1. 1.
    Sziranyi, T., Shadaydeh, M.: Segmentation of remote sensing images using similarity-measure-based fusion-mrf model. IEEE Geosci. Remote Sens. Lett. 11, 1544–1548 (2014)CrossRefGoogle Scholar
  2. 2.
    Ballesteros, R., Ortega, J.F., Hernandez, D., Moreno, M.A.: Onion biomass monitoring using uav-based rgb imaging. Precis. Agric. (2018)Google Scholar
  3. 3.
    Wei, Z., Han, Y., Li, M., Yang, K., Yang, Y., Luo, Y., Ong, S.H.: A small UAV based multi-temporal image registration for dynamic agricultural terrace monitoring. Remote Sens. 9, 904 (2017)CrossRefGoogle Scholar
  4. 4.
    Giordan, D., Manconib, A., Remondinoc, F., Nexd, F.: Use of unmanned aerial vehicles in monitoring application and management of natural hazards. Geomat. Nat. Haz. Risk 8, 1–4 (2017)CrossRefGoogle Scholar
  5. 5.
    Shadaydeh, M., Zlinszky, A., Manno-Kovacs, A., Sziranyi, T.: Wetland mapping by fusion of airborne laser scanning and multi-temporal multispectral satellite imagery. Int. J. Remote Sens. 38, 7422–7440 (2017)CrossRefGoogle Scholar
  6. 6.
    Inglada, J., Giros, A.: On the possibility of automatic multisensor image registration. IEEE Trans. Geosci. Remote Sens. 42, 2104–2120 (2004)CrossRefGoogle Scholar
  7. 7.
    Benedek, C., Shadaydeh, M., Kato, Z., Sziranyi, T., Zerubia, J.: Multilayer Markov random field models for change detection in optical remote sensing images. ISPRS J. Photogrammetry Remote Sens. 107, 22–37 (2015). Multitemporal remote sensing data analysisCrossRefGoogle Scholar
  8. 8.
    Benedek, C., Sziranyi, T.: Change detection in optical aerial images by a multilayer conditional mixed Markov model. IEEE Trans. Geosci. Remote Sens. 47, 3416–3430 (2009)CrossRefGoogle Scholar
  9. 9.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 721–741 (1984)CrossRefGoogle Scholar
  10. 10.
    Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for Markov Random Fields. In: 9th European Conference on Computer Vision, vol. 2, pp. 16–29 (2006)CrossRefGoogle Scholar
  11. 11.
    Kato, Z., Zerubia, J., Berthod, M.: Unsupervised parallel image classification using Markovian models. Pattern Recogn. 32, 591–604 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tamás Szirányi
    • 1
    Email author
  • Andras Kriston
    • 1
  • Andras Majdik
    • 1
  • Laszlo Tizedes
    • 1
  1. 1.MTA SZTAKIBudapestHungary

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