Journal of Siberian Federal University. Engineering & Technologies / The Effectiveness of Non-Parametric Classifiers in a Limited Training Set

Full text (.pdf)
Issue
Journal of Siberian Federal University. Engineering & Technologies. 2012 5 (5)
Authors
Romanov, Aleksey A.; Rubanov, Kirill A.
Contact information
Romanov, Aleksey A.: Siberian Federal University , 79 Svobodny, Krasnoyarsk, 660041 Russia , e-mail: ; Rubanov, Kirill A.: Siberian Federal University , 79 Svobodny, Krasnoyarsk, 660041 Russia
Keywords
remote sensing; pattern recognition; supervised classification; neural networks; support vector machine
Abstract

This paper presents a comparative analysis of the effectiveness of the method of support vector machine and artificial neural networks for classification of satellite images medium spatial resolution as an example of a high degree of heterogeneity and limited training data. The results of field-based researches have been used for test cases generation. Neural network approach showed the best result for classification accuracy (89,9 % vs. 86,2 % support vector), but was significantly less speed.

Pages
495-506
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/3194

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