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Computer Optics, 2021, Volume 45, Issue 6, Pages 887–896
DOI: https://doi.org/10.18287/2412-6179-CO-1038 (Mi co980) 		 
This article is cited in 13 scientific papers (total in 13 papers)

IMAGE PROCESSING, PATTERN RECOGNITION

Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index

N. A. Firsova, V. V. Podlipnovab, N. A. Ivlievab, P. Nikolaevc, S. V. Mashkovd, P. A. Ishkind, R. V. Skidanovab, A. V. Nikonorovab

a Samara National Research University
b Image Processing Systems Institute of the RAS - Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia, Samara
c Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow
d Samara State Agrarian University
Full-text PDF (8572 kB) Citations (13)
DOI: https://doi.org/10.18287/2412-6179-CO-1038
Abstract: In this paper, we propose an approach to the classification of high-resolution hyperspectral images in the applied problem of identification of vegetation types. A modified spectral-spatial con-volutional neural network with compensation for illumination variations is used as a classifier. For generating a training dataset, an algorithm based on an adaptive vegetation index is proposed. The effectiveness of the proposed approach is shown on the basis of survey data of agricultural lands obtained from a compact hyperspectral camera developed in-house.
Keywords: hyperspectral images, vegetation index, convolutional neural networks
Funding agency Grant number
Russian Science Foundation 20-69-47110
Russian Foundation for Basic Research 19-29-01235
Ministry of Education and Science of the Russian Federation 007-ГЗ/Ч3363/26
The theoretical part and neural network models were developed with the support from the Russian Science Foundation under RSF grant 20-69-47110. The experimental part was executed with the support from the Russian Foundation for Basic Research under the government project of the IPSI RAS -- a branch of the Federal Scientific-Research Center "Crystallography and Photonics" of the RAS (agreement № 007-ГЗ/Ч3363/26). The authors are grateful to E.P. Tsirulev, N.V. Borovkova and A.A. Solovyov for their help in the field work.
Received: 02.09.2021
Accepted: 06.09.2021
Document Type: Article
Language: Russian
Citation: N. A. Firsov, V. V. Podlipnov, N. A. Ivliev, P. Nikolaev, S. V. Mashkov, P. A. Ishkin, R. V. Skidanov, A. V. Nikonorov, “Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index”, Computer Optics, 45:6 (2021), 887–896
Citation in format AMSBIB
\Bibitem{FirPodIvl21}
\by N.~A.~Firsov, V.~V.~Podlipnov, N.~A.~Ivliev, P.~Nikolaev, S.~V.~Mashkov, P.~A.~Ishkin, R.~V.~Skidanov, A.~V.~Nikonorov
\paper Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index
\jour Computer Optics
\yr 2021
\vol 45
\issue 6
\pages 887--896
\mathnet{http://mi.mathnet.ru/co980}
\crossref{https://doi.org/10.18287/2412-6179-CO-1038}
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  • https://www.mathnet.ru/eng/co/v45/i6/p887
    • This publication is cited in the following 13 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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