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Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index
N. Firsov 1, V. Podlipnov 1,2, N. Ivliev 1,2, P. Nikolaev 3, S. Mashkov 4, P. Ishkin 4, R. Skidanov 1,2, A. Nikonorov 1

Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34,
IPSI RAS – Branch of FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151,

Institute for Information Transmission Problems, RAS,
127051, Moscow, Russia, Bolshoy Karetny per. 19, build. 1,

Samara State Agrarian University, 446442, Usty-Kinelyskiy, Russia, Uchebnaya 2

 PDF, 8572 kB

DOI: 10.18287/2412-6179-CO-1038

Pages: 887-896.

Full text of article: Russian language.

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 convolutional 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.

hyperspectral images, vegetation index, convolutional neural networks.

Firsov NA, Podlipnov VV, Ivliev NA, Nikolaev PP, Mashkov SV, Ishkin PA, Skidanov RV, Nikonorov AV. Neural network-aided classification of hyperspectral vegetation images with a training sample generated using an adaptive vegetation index. Computer Optics 2021; 45(6): 887-896. DOI: 10.18287/2412-6179-CO-1038.

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.


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