Nonlinear discriminant analysis and RVM for efficient classification of small land-cover patches

Hughes phenomenon is a serious problem in supervised classification of hyperspectral images in particular for small land-cover patches. A solution for this problem through integrating the capabilities of a nonlinear discriminating analysis with relevance vector machine (RVM) is proposed in this paper. It first transforms the hyperdimensional data to a new space with a better class separability. Then, a multiclass RVM classifier processes the transformed data for precise labeling of the classes. The results show that the proposed approach outperforms both RVM as well as support vector machine (SVM), when they are applied to the original hyperdimensional data space. Indeed, it is an advantage for key information detection in the classification context.