Adaptive Nonparametric Weighed Feature Extraction for Hyperspectral Image Classification

Abstract

In this study, a novel classifier ensemble method named adaptive nonparametric weighted feature extraction (AdaNWFE) is proposed. This new concept is deduced from AdaBoost and NWFE. The main idea of AdaNWFE is adaptive in the sense that subsequent feature spaces are tweaked in favor of those instances misclassified by classifiers in the previous feature space. All training samples are projected to these feature spaces to train various classifiers and then constitute a multiple classifier system. The experimental results based on two hyperspectral data sets show that the proposed algorithm can generate better classification results than only applying NWFE.