Hyperspectral image classification with mahalanobis relevance vector machines

This paper introduces the use of Relevance Vector Machines (RVM) for remote sensing hyperspectral image classification. We also include the Mahalanobis kernel in the formulation of the RVM to take into account the covariance of the features in the classification process. Experimental results in different scenarios confirm the accuracy and robustness of the proposed method, and also the ease of free parameters tuning.