Кл.слова (ненормированные):
Forestry -- Pixels -- Satellite imagery -- Vegetation -- Boreal forests -- Mixed forests -- Multi-spectral imagery -- Satellite images -- Spectral channels -- Trained neural networks -- Vegetation index -- Vegetation type -- Multilayer neural networks
Аннотация: The applicability of artificial neural networks (ANN) for the identification of vegetation types using satellite multispectral imagery was studied. The study was focused on the three main vegetation types found in the south of the Krasnoyarsk Region: mixed forest, boreal forest and grassland. Sentinel-2 satellite images were used as a data source for the neural networks. It was shown that vegetation type can be identified pixel-by-pixel using 12 spectral channels and simple feed forward ANN with good quality and reliability. Analysis of the input layer of the trained neural networks allowed several spectral bands to be selected that were the most valuable for the ANN decision and not used in the classic NDVI vegetation index. © 2019 IOP Publishing Ltd. All rights reserved.
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Держатели документа:
Institute of Biophysics, FRC KSC SB RAS, Akademgorodok 50/50, Krasnoyarsk, 660036, Russian Federation
Institute of Computation Modeling, FRC KSC SB RAS, Akademgorodok 50/44, Krasnoyarsk, 660036, Russian Federation
Доп.точки доступа:
Saltykov, M.; Yakubailik, O.; Bartsev, S.