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Application of Neural Network Technologies for the Classification of Cloudiness by Texture Parameters of MODIS High-Resolution Images

  • METHODS AND TOOLS FOR SPACE DATA PROCESSING AND INTERPRETATION
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Abstract

The technique of a search for images of cloudiness of various types from MODIS satellite images based on a comparison with archive data of observations on the network of meteorological stations is presented. Based on an expert estimate, 14 types of cloudiness possessing a unique structure on images recorded with a spatial resolution of 250 m are identified. Images of cloudiness of these types and results of investigations of their texture parameters found based on the statistical gray-level co-occurrences matrix (GLCM) approach are presented. For the indicated cloudiness types, characteristic texture features or their combinations are determined. To classify the cloudiness based on information on the texture parameters, it is proposed to use the neural network based on the three-layer perceptron. The modified method of adaptive tuning of the learning rate of the neural network is described. Results of cloudiness classification and their reliability are discussed.

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ACKNOWLEDGMENTS

We are grateful to T.M. Rasskazchikova for participating in peer inspections on the determination of cloudiness types with a unique image texture.

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Authors and Affiliations

  1. Tomsk State University of Control Systems and Radioelectronics, 634050, Tomsk, Russia

    V. G. Astafurov

  2. Zuev Institute of Atmospheric Optics, Siberian Branch, Russian Academy of Sciences, 634055, Tomsk, Russia

    V. G. Astafurov & A. V. Skorokhodov

Authors
  1. V. G. Astafurov
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  2. A. V. Skorokhodov
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Correspondence to V. G. Astafurov.

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Translated by A. Nikol’skii

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Astafurov, V.G., Skorokhodov, A.V. Application of Neural Network Technologies for the Classification of Cloudiness by Texture Parameters of MODIS High-Resolution Images. Izv. Atmos. Ocean. Phys. 55, 1012–1021 (2019). https://doi.org/10.1134/S000143381909007X

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