The effectiveness of non-parametric classifiers in a limited training set

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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.

Remote sensing, pattern recognition, supervised classification, neural networks, support vector machine

Короткий адрес: https://sciup.org/146114672

IDR: 146114672

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