Abstract
In hyperspectral image analysis, the classification task has generally been discussed with dimensionality reduction due to high correlation and noise between the spectral features, which might cause significantly low classification performance. In supervised classification, limited training samples in proportion to the number of spectral features have also negative impacts on the classification accuracy, which has known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and proposed a novel feature selection algorithm by using the method called random sampling high dimensional model representation (RS-HDMR), and the proposed algorithm were tested on a toy and hyperspectral dataset in comparison to conventional feature selection algorithms with regards to both computational time and classification accuracy.
Original language | English |
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Title of host publication | 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1108-1111 |
Number of pages | 4 |
ISBN (Electronic) | 9781479979295 |
DOIs | |
Publication status | Published - 10 Nov 2015 |
Event | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2015-November |
Conference
Conference | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 |
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Country/Territory | Italy |
City | Milan |
Period | 26/07/15 → 31/07/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- feature selection
- high dimensional model representation
- hyperspectral image classification
- Sensitivity analysis