NEURAL NETWORK CLASSIFICATION OF HYPERSPECTRAL IMAGES ON THE BASIS OF THE HILBERT-HUANG TRANSFORM
E. S. Nezhevenko, A. S. Feoktistov, O. Yu. Dashevskii
Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, prosp. Akademika Koptyuga, 1
Keywords: классификация, гиперспектральное изображение, преобразование Гильберта - Хуанга, главные компоненты, комплексные нейронные сети, classification, hyperspectral image, Hilbert-Huang transform, principal components, complex neural networks
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 as attributes and principal components without involving spatial information. Radial-basis and complex neural networks are used for classification.
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