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Pattern Recognition of Forest Vegetation from Hyperspectral Airborne and Multichannel Satellite Data of High Spatial Resolution: Comparison of Results and Estimation of Their Accuracy

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Abstract

The pattern recognition of a forest surface from remote sensing data (both airborne hyperspectral data and WorldView-2 multichannel satellite data of high spatial resolution) is investigated. Calculations have been performed using both the method developed earlier by the authors and the standard statistical approach. For three fragments of the forest surface, the range of changes in the accuracy of recognition calculations has been estimated for both airborne and satellite data, depending on the use of different databases developed for the recognition system. Some features of the pattern recognition of the underlying surface are discussed on the basis of both hyperspectral airborne data and multichannel satellite data of high spatial resolution.

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Funding

This study was supported by the Russian Foundation for Basic Research, project no. 19-01-00215.

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Authors and Affiliations

  1. Moscow State University, Moscow, Russia

    V. V. Kozoderov

  2. Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia

    V. D. Egorov

Authors
  1. V. V. Kozoderov
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  2. V. D. Egorov
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Correspondence to V. V. Kozoderov.

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Translated by V. Arutyunyan

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Kozoderov, V.V., Egorov, V.D. Pattern Recognition of Forest Vegetation from Hyperspectral Airborne and Multichannel Satellite Data of High Spatial Resolution: Comparison of Results and Estimation of Their Accuracy. Izv. Atmos. Ocean. Phys. 56, 1146–1158 (2020). https://doi.org/10.1134/S0001433820090133

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  • DOI: https://doi.org/10.1134/S0001433820090133

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