Universiti Teknologi Malaysia Institutional Repository

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

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

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Official URL: http://dx.doi.org/10.1109/AMS.2008.152

Abstract

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.

Item Type:Book Section
Additional Information:ISBN: 978-076953136-6; 2nd Asia International Conference on Modelling and Simulation, AMS 2008; Kuala Lumpur; 13 May 2008 through 15 May 2008
Uncontrolled Keywords:alpha particle spectrometers, asset management, classification (of information), feedforward neural networks, image enhancement, image reconstruction, image retrieval, independent component analysis, learning systems, particle spectrometers, radial basis function networks, remote sensing, simulated annealing, space optics, vectors
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:12764
Deposited By: Liza Porijo
Deposited On:28 Jun 2011 09:10
Last Modified:28 Jun 2011 09:10

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