A two-stage feature extraction for hyperspectral image data classification

Guey Shya Chen*, Li-Wei Ko, Bor Chen Kuo, Shu Chuan Shih

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

In this study, a two-stage feature extraction algorithm cooperated with feature selection is proposed for improving hyperspectral data classification. The first stage feature extraction extracts the features for separating all classes and second stage feature extraction extracts the features for separating individual pair of classes, which can not be well separated in first stage feature space. Then feature selection is applied for selecting the best features. Real data experimental result show that the proposed 2-stage feature extraction outperforms single stage feature extraction.

Original languageEnglish
Pages1212-1215
Number of pages4
StatePublished - 1 Dec 2004
Event2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 - Anchorage, AK, United States
Duration: 20 Sep 200424 Sep 2004

Conference

Conference2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004
CountryUnited States
CityAnchorage, AK
Period20/09/0424/09/04

Keywords

  • Feature extraction
  • Hyperspectral data classification

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