Hyperspectral Image Classification Using Kernel-based Nonparametric Weighted Feature Extraction

Published 1 July 2006

Bor‐Chen Kuo, C.-H. Li, Tian-Wei Sheu, Honghong Liao

DOI10.1109/igarss.2006.147

Citations22

TL;DR

It is shown that KNWFE is a generalization of original NWFE, which is a computationally efficient, robust and stable method for hyperspectral image classification.

Abstract

Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Some studies show that nonparametric weighted feature extraction (NWFE; Kuo and Landgrebe, 2004) is a powerful tool to extract hyperspectral image features for classification. Recently, some studies also show that kernel-based methods are computationally efficient, robust and stable for pattern analysis. In this study, a kernel-based NWFE (KNWFE) is proposed for hyperspectral image classification. In this paper, we show that KNWFE is a generalization of original NWFE.

Keywords

Computer ScienceEarth and Planetary SciencesEngineering

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