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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bor-Chen Kuo Hsiang-Chuan Liu Yu-Chen Hsieh Ruey-Ming Chao |
| Copyright Year | 2005 |
| Description | Author affiliation: Graduate Sch. of Educational Meas. & Stat., National Taichung Teachers Coll., Taiwan (Bor-Chen Kuo) |
| Abstract | In this paper, a weighted random subspace method (RSM) with automatic subspace dimensionality selection has been proposed for classifying hyperspectral image data. The dimensionality selection method is based on the importance distribution of dimensionality estimated by kernel smoothing technique during the algorithm training. Two feature weighting methods based on normalized re-substitution accuracy and Fisher's LDA separability are introduced for improving the original RSM. Experimental result shows that the proposed algorithm outperforms the original random subspace method. |
| File Size | 258117 |
| File Format | |
| ISBN | 0780390504 |
| DOI | 10.1109/IGARSS.2005.1526131 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-07-29 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hyperspectral imaging Image classification Statistics Educational institutions Kernel Smoothing methods Voting Bioinformatics Medical services Chaos |
| Content Type | Text |
| Resource Type | Article |
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