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 language | English |
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Pages | 1212-1215 |
Number of pages | 4 |
State | Published - 1 Dec 2004 |
Event | 2004 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 2004 → 24 Sep 2004 |
Conference
Conference | 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 |
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Country | United States |
City | Anchorage, AK |
Period | 20/09/04 → 24/09/04 |
Keywords
- Feature extraction
- Hyperspectral data classification