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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Печать: 1064-2315
ISSN Онлайн: 2163-9337

Выпуски:
Том 52, 2020 Том 51, 2019 Том 50, 2018 Том 49, 2017 Том 48, 2016 Том 47, 2015 Том 46, 2014 Том 45, 2013 Том 44, 2012 Том 43, 2011 Том 42, 2010 Том 41, 2009 Том 40, 2008 Том 39, 2007 Том 38, 2006 Том 37, 2005 Том 36, 2004 Том 35, 2003 Том 34, 2002 Том 33, 2001 Том 32, 2000 Том 31, 1999 Том 30, 1998 Том 29, 1997 Том 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v44.i5.70
pages 67-80

Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations

Javier Gallego
European Commission Joint Research Center (JRC), Ispra (Italy)
Alexey N. Kravchenko
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev
Nataliya N. Kussul
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev, Ukraine
Sergey V. Skakun
Institute of Space Research of National Academy of Sciences of Ukraine and National Space Agency of Ukraine, Kiev, Ukraine
Andrey Yu. Shelestov
National University of Life and Environmental Sciences of Ukraine, Kiev
Yulia A. Grypych
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev

Краткое описание

A problem of crop plants classification for three regions of Ukraine with an area of 78.500 km2 is considered. Classification is carried out using not a single satellite but a time series of satellite images. The used satellite data are characterized by different spatial resolution and temporal characteristics. By example of this problem we assessed the efficiency of different classification algorithms (neural networks, decision trees and support vector machines) for substantially different data levels (of training and testing samples) both for extremely large data sets and under the condition of their lack (absence).


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