RVM‐based multi‐class classification of remotely sensed data

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.

Acknowledgments

I am grateful for the ATM and ground datasets that were acquired as part of the Commission of the European Community's AgriSAR campaign. The analyses were undertaken with the M‐RVM software. I am also grateful to the three referees for their helpful comments.