Support vector machines for classification in remote sensing

Support vector machines (SVM) represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of two experiments in which multi-class SVMs are compared with maximum likelihood (ML) and artificial neural networ...

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Veröffentlicht in:	International journal of remote sensing 2005-03, Vol.26 (5), p.1007-1011
Hauptverfasser:	Pal, M., Mather, P. M.
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Sprache:	eng
Schlagworte:	Applied geophysics Earth sciences Earth, ocean, space Exact sciences and technology Internal geophysics
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container_title	International journal of remote sensing
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creator	Pal, M. Mather, P. M.
description	Support vector machines (SVM) represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of two experiments in which multi-class SVMs are compared with maximum likelihood (ML) and artificial neural network (ANN) methods in terms of classification accuracy. The two land cover classification experiments use multispectral (Landsat-7 ETM+) and hyperspectral (DAIS) data, respectively, for test areas in eastern England and central Spain. Our results show that the SVM achieves a higher level of classification accuracy than either the ML or the ANN classifier, and that the SVM can be used with small training datasets and high-dimensional data.
doi_str_mv	10.1080/01431160512331314083
format	Article
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M.</creatorcontrib><title>Support vector machines for classification in remote sensing</title><title>International journal of remote sensing</title><description>Support vector machines (SVM) represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of two experiments in which multi-class SVMs are compared with maximum likelihood (ML) and artificial neural network (ANN) methods in terms of classification accuracy. The two land cover classification experiments use multispectral (Landsat-7 ETM+) and hyperspectral (DAIS) data, respectively, for test areas in eastern England and central Spain. 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subjects	Applied geophysics Earth sciences Earth, ocean, space Exact sciences and technology Internal geophysics
title	Support vector machines for classification in remote sensing
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