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Classification of Hyperspectral Images with Different Methods of Training Set Formation

  • Analysis and Synthesis of Signals and Images
  • Published:
Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

The efficiency of the methods of controlled spectral and spectral-spatial classification of vegetation types on the basis of hyperspectral pictures with different methods of training set formation is evaluated. The dependence of the classification accuracy on the number of spectral features is considered. It is shown that simultaneous allowance for spatial and spectral features ensures highquality classification of similarly looking types of vegetation by merely using training sets with the maximum degree of the pixel distribution over the image.

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Authors and Affiliations

  1. Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, pr. Akademika Koptyuga 1, Novosibirsk, 630090, Russia

    S. M. Borzov & O. I. Potaturkin

  2. Novosibirsk State University, ul. Pirogova 2, Novosibirsk, 630090, Russia

    O. I. Potaturkin

Authors
  1. S. M. Borzov
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  2. O. I. Potaturkin
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Corresponding author

Correspondence to S. M. Borzov.

Additional information

Original Russian Text © S.M. Borzov, O.I. Potaturkin, 2018, published in Avtometriya, 2018, Vol. 54, No. 1, pp. 89–97.

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Borzov, S.M., Potaturkin, O.I. Classification of Hyperspectral Images with Different Methods of Training Set Formation. Optoelectron.Instrument.Proc. 54, 76–82 (2018). https://doi.org/10.3103/S8756699018010120

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  • DOI: https://doi.org/10.3103/S8756699018010120

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