Regularized Feature Extractions for Hyperspectral Data Classification

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

*此作品的通信作者

研究成果: Paper同行評審

12 引文 斯高帕斯(Scopus)

摘要

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).

原文English
頁面1767-1769
頁數3
出版狀態Published - 24 十一月 2003
事件2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France
持續時間: 21 七月 200325 七月 2003

Conference

Conference2003 IGARSS: Learning From Earth's Shapes and Colours
國家/地區France
城市Toulouse
期間21/07/0325/07/03

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