Random Subspace Ensemble With Enhanced Feature for Hyperspectral Image Classification

Abstract

In this letter, we propose a new hyperspectral image (HSI) classification approach, called the random subspace ensemble with enhanced feature (RSE-EF), which trains several individual classifiers with enhanced spatial information. The proposed approach aims to address two common issues: the curses of the imbalanced training samples and high feature-to-instance ratio. Specifically, we first propose a similar-neighboring-sample-search (SNSS) method to address the issue of imbalanced training samples. Afterward, we generate the enhanced random subspaces (ERSs) that possess relatively lower dimensionality and more distinctive information compared with the original random subspaces (RSs) so as to alleviate the curse of high feature-to-instance ratio more effectively. Furthermore, a shallow neural network kernel-based extreme learning machine (KELM) is applied to the RSE-EF to classify image pixels. Experimental results on two public hyperspectral data sets illustrate that the proposed RSE-EF approach outperforms the state-of-the-art HSI classification counterparts.