RVM-based multi-class classification of remotely sensed data

Foody, Giles M.

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.

Citation

Foody, G. M. (2008). RVM-based multi-class classification of remotely sensed data. International Journal of Remote Sensing, 29(6), https://doi.org/10.1080/01431160701822115

• Journal Article Type: Article
• Publication Date: Jan 1, 2008
• Deposit Date: Jun 14, 2013
• Publicly Available Date: Jun 14, 2013
• Journal: International Journal of Remote Sensing
• Print ISSN: 0143-1161
• Electronic ISSN: 1366-5901
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 6
• DOI: https://doi.org/10.1080/01431160701822115
• Public URL: https://nottingham-repository.worktribe.com/output/1015786
• Publisher URL: http://www.tandfonline.com/doi/full/10.1080/01431160701822115
• 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.com/10.1080/01431160701822115