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Spectral angle based kernels for the classification of hyperspectral images using support vector machines

Support vector machines (SVM) have been extensively used for classification purposes in a broad range of applications. These learning machines base their classification on the Euclidean distance of the data vectors or their dot products. These measures do not account for the spectral signature infor...

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Main Authors: Sap, M. N. N., Kohram, Mojtaba
Format: Book Section
Published: Institute of Electrical and Electronics Engineers 2008
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/12764/
http://dx.doi.org/10.1109/AMS.2008.152
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id my.utm.12764
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spelling my.utm.127642011-06-28T09:10:25Z http://eprints.utm.my/id/eprint/12764/ Spectral angle based kernels for the classification of hyperspectral images using support vector machines Sap, M. N. N. Kohram, Mojtaba QA75 Electronic computers. Computer science Support vector machines (SVM) have been extensively used for classification purposes in a broad range of applications. These learning machines base their classification on the Euclidean distance of the data vectors or their dot products. These measures do not account for the spectral signature information that can be achieved from remote sensing images. Given the high value of this information, integrating it into the SVM algorithm is a reasonable suggestion. This paper utilizes the spectral angle (SA) function as a measure for classification of a hyperspectral image. The SA function is joined together with the radial basis function (RBF) to form a spectral angle based RBF function. Experimentation results are promising and confirm that this approach can compete with existing classification methods. Institute of Electrical and Electronics Engineers 2008 Book Section PeerReviewed Sap, M. N. N. and Kohram, Mojtaba (2008) Spectral angle based kernels for the classification of hyperspectral images using support vector machines. In: Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008. Institute of Electrical and Electronics Engineers, New York, 559 -563. ISBN 978-076953136-6 http://dx.doi.org/10.1109/AMS.2008.152 doi:10.1109/AMS.2008.152
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sap, M. N. N.
Kohram, Mojtaba
Spectral angle based kernels for the classification of hyperspectral images using support vector machines
description Support vector machines (SVM) have been extensively used for classification purposes in a broad range of applications. These learning machines base their classification on the Euclidean distance of the data vectors or their dot products. These measures do not account for the spectral signature information that can be achieved from remote sensing images. Given the high value of this information, integrating it into the SVM algorithm is a reasonable suggestion. This paper utilizes the spectral angle (SA) function as a measure for classification of a hyperspectral image. The SA function is joined together with the radial basis function (RBF) to form a spectral angle based RBF function. Experimentation results are promising and confirm that this approach can compete with existing classification methods.
format Book Section
author Sap, M. N. N.
Kohram, Mojtaba
author_facet Sap, M. N. N.
Kohram, Mojtaba
author_sort Sap, M. N. N.
title Spectral angle based kernels for the classification of hyperspectral images using support vector machines
title_short Spectral angle based kernels for the classification of hyperspectral images using support vector machines
title_full Spectral angle based kernels for the classification of hyperspectral images using support vector machines
title_fullStr Spectral angle based kernels for the classification of hyperspectral images using support vector machines
title_full_unstemmed Spectral angle based kernels for the classification of hyperspectral images using support vector machines
title_sort spectral angle based kernels for the classification of hyperspectral images using support vector machines
publisher Institute of Electrical and Electronics Engineers
publishDate 2008
url http://eprints.utm.my/id/eprint/12764/
http://dx.doi.org/10.1109/AMS.2008.152
_version_ 1643646035366510592
score 13.209306

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