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Hyperspectral Image Classification Using Kernel-based Nonparametric Weighted Feature Extraction

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4 Author(s)
Bor-Chen Kuo ; Grad. Sch. of Educ. Meas. & Stat., Nat. Taichung Univ., Taichung ; Cheng-Hsuan Li ; Tian-Wei Sheu ; Hsueh-Hua Liao

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.

Published in:

Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on

Date of Conference:

July 31 2006-Aug. 4 2006