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RVM-based multi-class classification of remotely sensed data

Foody, Giles M.

Authors

Giles M. Foody giles.foody@nottingham.ac.uk



Abstract

The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi-class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per-case basis and help to identify possible routes to further enhance classification accuracy.

Journal Article Type Article
Publication Date Jan 1, 2008
Journal International Journal of Remote Sensing
Print ISSN 0143-1161
Electronic ISSN 0143-1161
Publisher Taylor & Francis
Peer Reviewed Peer Reviewed
Volume 29
Issue 6
APA6 Citation Foody, G. M. (2008). RVM-based multi-class classification of remotely sensed data. International Journal of Remote Sensing, 29(6), doi:10.1080/01431160701822115
DOI https://doi.org/10.1080/01431160701822115
Publisher URL http://www.tandfonline.com/doi/full/10.1080/01431160701822115
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information This is an Author's Accepted Manuscript of an article published in International Journal of Remote Sensing: Foody, G.M., RVM-based multi-class classification of remotely sensed data, International Journal of Remote Sensing, 29(6), 2008, copyright Taylor & Francis, available online at: http://www.tandfonline.....1080/01431160701822115

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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