Usage of different neural networks in identification of plant types

Since introduction of neural networks into remote sensing they demonstrate good efficiency in remote sensing data analysis. This work is devoted to processing of multispectral (12 bands) images from Sentinel-2(A, B) satellites. Satellite images of areas in Krasnoyarsk Region and Khakassia with known...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-01, Vol.734 (1), p.12097
Main Authors: Bartsev, S, Ivanova, Y, Saltykov, M
Format: Article
Language:English
Subjects:
Data analysis
Neural networks
Remote sensing
Satellite imagery
Satellites
Vegetation
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Summary:Since introduction of neural networks into remote sensing they demonstrate good efficiency in remote sensing data analysis. This work is devoted to processing of multispectral (12 bands) images from Sentinel-2(A, B) satellites. Satellite images of areas in Krasnoyarsk Region and Khakassia with known vegetation types are used as task books to train neural networks. Trained neural networks have been reduced to determine which bands are significant for vegetation type identification. Reduction of trained neural network show that vegetation type can be determined from only four infrared bands without significant loses in performance in comparison with non-reduced neural network.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/734/1/012097