Abstract
The method of image classification with its preliminary transformation to principal components and with the use of the Hilbert–Huang transform is studied by an example of neural network classification of a hyperspectral image. The efficiency of the method is demonstrated through comparisons with traditional methods of neural network classification with the use of spectral components and principal components without involving spatial information as features. Radial-basis and complex neural networks are used for classification.
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Original Russian Text © E.S. Nezhevenko, A.S. Feoktistov, O.Yu. Dashevskii, 2017, published in Avtometriya, 2017, Vol. 53, No. 2, pp. 79–85.
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Nezhevenko, E.S., Feoktistov, A.S. & Dashevskii, O.Y. Neural network classification of hyperspectral images on the basis of the Hilbert–Huang transform. Optoelectron.Instrument.Proc. 53, 165–170 (2017). https://doi.org/10.3103/S8756699017020091
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DOI: https://doi.org/10.3103/S8756699017020091