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Proceedings Paper

Neural network technologies for image classification
Author(s): A. M. Korikov; A. V. Tungusova
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Paper Abstract

We analyze the classes of problems with an objective necessity to use neural network technologies, i.e. representation and resolution problems in the neural network logical basis. Among these problems, image recognition takes an important place, in particular the classification of multi-dimensional data based on information about textural characteristics. These problems occur in aerospace and seismic monitoring, materials science, medicine and other. We reviewed different approaches for the texture description: statistical, structural, and spectral. We developed a neural network technology for resolving a practical problem of cloud image classification for satellite snapshots from the spectroradiometer MODIS. The cloud texture is described by the statistical characteristics of the GLCM (Gray Level Co- Occurrence Matrix) method. From the range of neural network models that might be applied for image classification, we chose the probabilistic neural network model (PNN) and developed an implementation which performs the classification of the main types and subtypes of clouds. Also, we chose experimentally the optimal architecture and parameters for the PNN model which is used for image classification.

Paper Details

Date Published: 19 November 2015
PDF: 4 pages
Proc. SPIE 9680, 21st International Symposium Atmospheric and Ocean Optics: Atmospheric Physics, 968023 (19 November 2015); doi: 10.1117/12.2205896
Show Author Affiliations
A. M. Korikov, National Research Tomsk Polytechnic Univ. (Russian Federation)
Tomsk State Univ. of Control Systems and Radio Electronics (Russian Federation)
A. V. Tungusova, National Research Tomsk Polytechnic Univ. (Russian Federation)
Tomsk State Univ. of Control Systems and Radio Electronics (Russian Federation)


Published in SPIE Proceedings Vol. 9680:
21st International Symposium Atmospheric and Ocean Optics: Atmospheric Physics
Oleg A. Romanovskii, Editor(s)

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