Hyperspectral image classification using relevance vector machines

Demir B., Erturk S.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol.4, no.4, pp.586-590, 2007 (SCI-Expanded) identifier identifier


This letter presents a hyperspectral image classification method based on relevance vector machines (RVMs). Support vector machine (SVM)-based approaches have been recently proposed for hyperspectral image classification and have raised important interest. In this letter, it is genuinely proposed to use an RVM-based approach for the classification of hyperspectral images. It is shown that approximately the same classification accuracy is obtained using RVM-based classification, with a significantly smaller relevance Vector rate and, therefore, much faster testing time, compared with SVM-based classification. This feature makes the RVM-based hyperspectral classification approach more suitable for applications that require low complexity and, possibly, real-time classification.