Regularized Feature Extractions for Hyperspectral Data Classification

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

*Corresponding author for this work

Research output: Contribution to conference › Paper

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 languageEnglish
Pages1767-1769
Number of pages3
StatePublished - 24 Nov 2003
Event2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France
Duration: 21 Jul 2003 → 25 Jul 2003

Conference

Conference2003 IGARSS: Learning From Earth's Shapes and Colours
CountryFrance
CityToulouse
Period21/07/03 → 25/07/03

Keywords

  • Feature extraction
  • Hyperspectral data classification
  • Regularization

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    Kuo, B. C., Ko, L-W., Pai, C. H., & Landgrebe, D. A. (2003). Regularized Feature Extractions for Hyperspectral Data Classification. 1767-1769. Paper presented at 2003 IGARSS: Learning From Earth's Shapes and Colours, Toulouse, France.