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Preprints of the Keldysh Institute of Applied Mathematics, 2021, 028, 21 pp.
DOI: https://doi.org/10.20948/prepr-2021-28
(Mi ipmp2946)
 

Multilayered autoencoders in problems of hyperspectral image analysis and processing

M. G. Kuzmina
References:
Abstract: A model of five-layered autoencoder (stacked autoencoder, SAE) is suggested for deep image features extraction and deriving compressed hyperspectral data set specifying the image. Spectral cost function, dependent on spectral curve forms of hyperspectral image, has been used for the autoencoder tuning. At the first step the autoencoder capabilities will be tested based on using pure spectral information contained in image data. The images from well known and widely used hyperspectral databases (Indian Pines, Pavia University è KSC) are planned to be used for the model testing.
Keywords: deep neural networks, multilayered autoencoders (stacked autoencoders, SAE), hóperspectral images, image feature extraction, hóperspectral data compression.
Document Type: Preprint
Language: Russian
Citation: M. G. Kuzmina, “Multilayered autoencoders in problems of hyperspectral image analysis and processing”, Keldysh Institute preprints, 2021, 028, 21 pp.
Citation in format AMSBIB
\Bibitem{Kuz21}
\by M.~G.~Kuzmina
\paper Multilayered autoencoders in problems of hyperspectral image analysis and processing
\jour Keldysh Institute preprints
\yr 2021
\papernumber 028
\totalpages 21
\mathnet{http://mi.mathnet.ru/ipmp2946}
\crossref{https://doi.org/10.20948/prepr-2021-28}
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