Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Assessment of the effectiveness of support vector machines for hyperspectral data
Future Generation Computer Systems - Special issue: Geocomputation
Harshness in image classification accuracy assessment
International Journal of Remote Sensing
International Journal of Remote Sensing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
SVM-based segmentation and classification of remotely sensed data
International Journal of Remote Sensing
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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.