Testing the suitability of v-Support Vector Machine for hyperspectral image classification

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Okwuashi, Onuwa
Ndehedehe, Christopher
Olayinka, Dupe
Akpomrere, Rufus
Eyo, Etim
Ogbijara, Ufuoma
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2024
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Abstract

This research presents the novel application of v-Support Vector Machine (v-SVM) to hyperspectral image classification. The essence of this work is to test the suitability of v-SVM for hyperspectral image classification. The v-SVM provides an enhancement to the regularisation parameter in the classical Support Vector Machine (SVM). The regularisation parameter controls the trade-off between obtaining a high training error and a low training error which is the ability of the model to generalise the unseen data (or test data). The value of the regularisation parameter in the classical SVM ranges from 0 to +∞ ; this makes it usually challenging to determine the most appropriate optimal regularisation parameter value. The invention of the v-SVM has made it easier to find an appropriate optimal regularisation parameter value since the regularisation parameter is narrowed down from a wide range of 0 to +∞ to a narrow range of 0 to 1 . In this study, two hyperspectral images of Indian Pines region in Northwest Indiana, USA and University of Pavia, Italy are used as test beds for the experiment. The result of the experiment shows that the v-SVM performed fairly better than the classical SVM; however, it fell short of the notable conventional classifiers.

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International Journal of Image and Data Fusion

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© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

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This publication has been entered in Griffith Research Online as an advance online version.

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Machine learning

Photogrammetry and remote sensing

Geomatic engineering

Environmental assessment and monitoring

Data mining and knowledge discovery

Earth sciences

Earth and space science informatics

Information and computing sciences

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Okwuashi, O; Ndehedehe, C; Olayinka, D; Akpomrere, R; Eyo, E; Ogbijara, U, Testing the suitability of v-Support Vector Machine for hyperspectral image classification, International Journal of Image and Data Fusion, 2024, pp. 1-14

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