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

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 genera...

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Published inKompʹûternaâ optika Vol. 45; no. 6; pp. 887 - 896
Main Authors Firsov, N., Podlipnov, V., Ivliev, N., Nikolaev, P., Mashkov, S., Ishkin, P., Skidanov, R., Nikonorov, A.
Format Journal Article
LanguageEnglish
Russian
Published Samara National Research University 01.12.2021
Subjects
convolutional neural networks
hyperspectral images
vegetation index
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Summary: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.
ISSN:0134-2452
2412-6179
DOI:10.18287/2412-6179-CO-1038