Izvestiya, Atmospheric and Oceanic Physics

, Volume 53, Issue 9, pp 1132–1141 | Cite as

Comparison Analysis of Recognition Algorithms of Forest-Cover Objects on Hyperspectral Air-Borne and Space-Borne Images

  • V. V. Kozoderov
  • T. V. Kondranin
  • E. V. Dmitriev
Methods and Means of Processing and Interpretation of Space Information


The basic model for the recognition of natural and anthropogenic objects using their spectral and textural features is described in the problem of hyperspectral air-borne and space-borne imagery processing. The model is based on improvements of the Bayesian classifier that is a computational procedure of statistical decision making in machine-learning methods of pattern recognition. The principal component method is implemented to decompose the hyperspectral measurements on the basis of empirical orthogonal functions. Application examples are shown of various modifications of the Bayesian classifier and Support Vector Machine method. Examples are provided of comparing these classifiers and a metrical classifier that operates on finding the minimal Euclidean distance between different points and sets in the multidimensional feature space. A comparison is also carried out with the “K-weighted neighbors” method that is close to the nonparametric Bayesian classifier.


hyperspectral air-borne and space-borne imagery natural-and anthropogenic-object pattern recognition optimization of data processing 


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Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • V. V. Kozoderov
    • 1
  • T. V. Kondranin
    • 2
  • E. V. Dmitriev
    • 3
  1. 1.Moscow State UniversityMoscowRussia
  2. 2.Moscow Institute for Physics and Technology (State University)DolgoprudnyRussia
  3. 3.Institute of Numerical MathematicsRussian Academy of SciencesMoscowRussia

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