Increasing Classification Accuracy of Relevance Vector Machine on Hyperspectral Images with Preprocessing

Abstract

In this paper, it is proposed to apply Principal Component Analysis (PCA) and Mathematical Morphology operations in order to increase classification performance and decrease computational load of Relevance Vector Machine (RVM). As preprocessing operations, by using PCA, the number of bands is reduced and by using morphological operations, it becomes possible to use spatial informations of data in additional to the spectral informations that the data has already had. The bands obtained by morphological operations using the results of PCA are processed in RVM Proposed method shows that the bands obtained after preprocessing is giving better results than the RVM applied to the data directly.