Recursive feature selection based on non-parallel SVMs and its application to hyperspectral image classification

Abstract

In classification of hyperspectral image, a common challenge is to deal with Hughes phenomenon also known curse of dimensionality, which is caused by high dimension with low samples and resulting in a poor classification performance [1]. There have been many ongoing researches in the literature to mitigate the Hughes phenomenon and accordingly increase the classification performance [2], [3], [4]. Support vector machines (SVM) is the one of the most important algorithm used in the classification of hyper-spectral image which is generally not effected by curse of dimensionality. Although it provides a good generalization ability in classification of hyperspectral dataset, recently, in order to increase the performance of SVM with the limited training data, a recursive feature elimination (RFE) approach based on SVM classifier has been introduced in order to rank the features with respect to their contribution to classification performance [5]. RFE approach utilize the objective function as a feature ranking criterion in order to eliminate the redundant features, and to produce a list of features having more discriminant ability. The experiments in the hyperspectral data classification by SVM also showed that the SVM-RFE method does not affected from the curse of dimensionality even if the number of samples are limited, and the satisfactory classification performance is obtained with using a small number of features [6].