Cognitive technologies for processing optical images of high spatial and spectral resolution

Abstract

The main stages of the development of technologies for natural and anthropogenic object recognition (cognitive technologies for optical image processing) using remote sensing data are considered together with computational procedures for atmospheric correction of multispectral and hyperspectral air-space images. The main focus is on recognizing forest ecosystems of various species and age, based on inflight testing of domestic hyperspectral equipment for a selected test area, where ground-based forest inventory and other observations were carried out. High accuracies of the recognition of separate gradations of ages for the selected pure birch and pine stands are revealed using elaborated software for airborne hyperspectral image processing.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    R. Duda and P. Khart, Image Recognition and Scene Analysis (Mir, Moscow, 1976) [in Russian].

    Google Scholar 

  2. 2.

    V. V. Kozoderov, “Estimate of the distorting effect of the atmosphere during interpretation of space images of nature formations,” in Space Researches of the Earth, Video Data Processing Using Computers (Nauka, Moscow, 1978), pp. 24–35 [in Russian].

    Google Scholar 

  3. 3.

    A. K. Jain, “Advances in mathematical models in image processing,” Proc. IEEE 69, 502–528 (1981).

    ADS  Article  Google Scholar 

  4. 4.

    N. S. Friedland and A. Rosenfeld, “Compact object recognition using energy-function based optimization,” IEEE Trans. Pattern Anal. Machine Intell. 14(7), 770–777 (1992).

    Article  Google Scholar 

  5. 5.

    B. Tso and R. C. Olsen, “A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process,” Remote Sens. Environ. 97, 127–136 (2005).

    Article  Google Scholar 

  6. 6.

    V. V. Kozoderov, “Atmospheric correction of images,” Issled. Zemli Kosmosa, No. 2, 65–75 (1983).

    Google Scholar 

  7. 7.

    X. W. Li, A. H. Strahler, and C. E. Woodcock, “A hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinuous canopies,” IEEE Trans. Geosci. Remote Sens. 33(2), 466–480 (1995).

    ADS  Article  Google Scholar 

  8. 8.

    K. Ya. Kondratyev, V. V. Kozoderov, and O. I. Smokty, Remote Sensing of the Earth from Space: Atmospheric Correction (Springer-Verlag, Heidelberg, 1992).

    Google Scholar 

  9. 9.

    D. W. Deering, “Field measurements of directional reflectance,” in Theory and Applications of Optical Remote Sensing (John Wiley & Sons, New York, 1989).

    Google Scholar 

  10. 10.

    P. J. Curran, G. M. Foody, K. Ya. Kondratyev, V. V. Kozoderov, and P. P. Fedchenko, Remote Sensing of Soils and Vegetation in the USSR (Taylor and Francis, London, 1990).

    Google Scholar 

  11. 11.

    N. Breda, “Ground-based measurements of leaf area index: A review of methods, instruments and current controversies,” J. Experim. Botany 54(392), 2403–2417 (2003).

    Article  Google Scholar 

  12. 12.

    V. V. Kozoderov, “Features of implementation of models for estimating plant biomass from satellite observations,” Issled. Zemli Kosmosa, No. 2, 79–88 (2006).

    Google Scholar 

  13. 13.

    V. V. Kozodyorov and V. S. Kosolapov, “Optical remote sensing of the biosphere with the help of multispectral aerospace images,” Opt. Atmos. 5(8), 550–554 (1992).

    Google Scholar 

  14. 14.

    V. V. Kozoderov, “A scientific approach to employ monitoring and modeling techniques for global change and terrestrial ecosystems and other related projects,” J. Biogeogr. 22(415), 927–933 (1995).

    Article  Google Scholar 

  15. 15.

    S. D. Prince and C. O. Justice, “Coarse resolution remote sensing of the Sahelian environment,” Int. J. Remote Sens. 12(6), 1133–1421 (1991).

    Article  Google Scholar 

  16. 16.

    V. V. Kozoderov and E. V. Dmitriev, “Remote sensing of soils and vegetation: Regional aspects,” Int. J. Remote Sens. 29(9), 2733–2748 (2008).

    ADS  Article  Google Scholar 

  17. 17.

    V. V. Kozoderov and E. V. Dmitriev, “Remote sensing of soils and vegetation: Pattern recognition and forest stand structure assessment,” Int. J. Remote Sens. 32(20), 5699–5717 (2011).

    ADS  Article  Google Scholar 

  18. 18.

    S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer-Verlag, New York; Berlin; Heidelberg; Tokyo, 1995).

    Book  Google Scholar 

  19. 19.

    V. V. Kozoderov, “Application of optical remote sensing data to study natural and climate processes,” Klimat Priroda 3(2), 3–16 (2012).

    Google Scholar 

  20. 20.

    V. V. Kozoderov, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing aircraft images of forest ecosystems using highly spectrally and spatially resolved data,” Issled. Zemli Kosmosa, No. 6, 57–64 (2013).

    Google Scholar 

  21. 21.

    V. V. Kozoderov, T. V. Kondranin, and E. V. Dmitriev, Thematic Processing of Multispectral and Hyperspectral Aerospace Images (MFTI, Moscow, 2013) [in Russian].

    Google Scholar 

  22. 22.

    V. V. Belov and M. V. Tarasenkov, “On the accuracy and operation speed of RTM algorithms for atmospheric correction of satellite images in the visible and UVranges,” Atmos. Ocean. Opt. 27(1), 54–61 (2014).

    Article  Google Scholar 

  23. 23.

    V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, O. Yu. Kazantsev, I. V. Persev, and M. V. Shcherbakov, “Processing of hyperspectral sounding data,” Issled. Zemli Kosmosa, No. 5, 3–11 (2012).

    Google Scholar 

  24. 24.

    A. V. Anishchenko, S. M. Ogreb, and P. M. Yukhno, “Comparative analysis of panchromatic and multispectral modes of spatial objects detection,” Atmos. Ocean. Opt. 27(1), 62–67 (2014).

    Article  Google Scholar 

  25. 25.

    T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, New York, 2001).

    Book  MATH  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to V. V. Kozoderov.

Additional information

Original Russian Text © V.V. Kozoderov, E.V. Dmitriev, V.P. Kamentsev, 2014, published in Optika Atmosfery i Okeana.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kozoderov, V.V., Dmitriev, E.V. & Kamentsev, V.P. Cognitive technologies for processing optical images of high spatial and spectral resolution. Atmos Ocean Opt 27, 558–565 (2014). https://doi.org/10.1134/S1024856014060116

Download citation

Keywords

  • remote sensing
  • optical images
  • pattern recognition
  • forest canopies of various species and age