Open AccessUsage of different neural networks in identification of plant types
Author(s) -
С. И. Барцев,
Yulia Ivanova,
Mikhail Saltykov
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/734/1/012097
Subject(s) - artificial neural network , vegetation (pathology) , multispectral image , identification (biology) , computer science , remote sensing , satellite , task (project management) , vegetation types , artificial intelligence , reduction (mathematics) , pattern recognition (psychology) , geography , mathematics , engineering , ecology , biology , medicine , geometry , systems engineering , pathology , aerospace engineering , habitat
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.