Regularized Feature Extractions for Hyperspectral Data Classification

Bor Chen Kuo; Li-Wei Ko; Chia Hao Pai; David A. Landgrebe

Regularized Feature Extractions for Hyperspectral Data Classification

Bor Chen Kuo^*, Li-Wei Ko, Chia Hao Pai, David A. Landgrebe

^*Corresponding author for this work

Institute of Bioinformatics and Systems Biology

Research output: Contribution to conference › Paper › peer-review

12 Scopus citations

Abstract

The regularized feature extraction methods for hyperspectral data classification were studied. The regularization algorithms worked for both parametric and nonparametric within-class scatter matrix. Real data experiment and simulated results show that the nonparametric weighted feature extraction (NWFE) is better method than the nonparametric discriminant analysis (NDA) and discriminant analysis feature extraction (DAFE).

Original language	English
Pages	1767-1769
Number of pages	3
State	Published - 24 Nov 2003
Event	2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France Duration: 21 Jul 2003 → 25 Jul 2003

Conference

Conference	2003 IGARSS: Learning From Earth's Shapes and Colours
Country	France
City	Toulouse
Period	21/07/03 → 25/07/03

Keywords

Feature extraction
Hyperspectral data classification
Regularization

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title = "Regularized Feature Extractions for Hyperspectral Data Classification",

abstract = "The regularized feature extraction methods for hyperspectral data classification were studied. The regularization algorithms worked for both parametric and nonparametric within-class scatter matrix. Real data experiment and simulated results show that the nonparametric weighted feature extraction (NWFE) is better method than the nonparametric discriminant analysis (NDA) and discriminant analysis feature extraction (DAFE).",

keywords = "Feature extraction, Hyperspectral data classification, Regularization",

author = "Kuo, {Bor Chen} and Li-Wei Ko and Pai, {Chia Hao} and Landgrebe, {David A.}",

year = "2003",

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T1 - Regularized Feature Extractions for Hyperspectral Data Classification

AU - Kuo, Bor Chen

AU - Ko, Li-Wei

AU - Pai, Chia Hao

AU - Landgrebe, David A.

PY - 2003/11/24

Y1 - 2003/11/24

N2 - The regularized feature extraction methods for hyperspectral data classification were studied. The regularization algorithms worked for both parametric and nonparametric within-class scatter matrix. Real data experiment and simulated results show that the nonparametric weighted feature extraction (NWFE) is better method than the nonparametric discriminant analysis (NDA) and discriminant analysis feature extraction (DAFE).

AB - The regularized feature extraction methods for hyperspectral data classification were studied. The regularization algorithms worked for both parametric and nonparametric within-class scatter matrix. Real data experiment and simulated results show that the nonparametric weighted feature extraction (NWFE) is better method than the nonparametric discriminant analysis (NDA) and discriminant analysis feature extraction (DAFE).

KW - Feature extraction

KW - Hyperspectral data classification

KW - Regularization

UR - http://www.scopus.com/inward/record.url?scp=0242710914&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0242710914

SP - 1767

EP - 1769

Y2 - 21 July 2003 through 25 July 2003

ER -

Regularized Feature Extractions for Hyperspectral Data Classification

Abstract

Conference

Keywords

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