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dc.contributor.authorChen, Jinzh_CN
dc.contributor.authorWang, Chengzh_CN
dc.contributor.author王程zh_CN
dc.contributor.authorWang, Runshengzh_CN
dc.date.accessioned2013-12-12T02:49:47Z
dc.date.available2013-12-12T02:49:47Z
dc.date.issued2009zh_CN
dc.identifier.citation2009 Ieee International Conference on Robotics and Biomimetics (Robio 2009)1372-1375zh_CN
dc.identifier.otherWOS:000285530501023zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/70982
dc.description.abstractDiscrete wavelet transform (DWT) provide a multiresolution view of hyperspectral data. This paper proposes a method to combine the wavelet features at different layer to improve the classification accuracy of hyperspectral data, where both global and local spectral features could be exploited. After feature extraction using DWT, the wavelet feature set of each layer is processed independently by support vector machines (SVMs). Then, the probability outputs of SVMs at each layer are fused to get the final class probability, and the classification result will be the class label with the maximum final class probability. Experimented with the Washington DC Mall hyperspectral data, the results demonstrate that the proposed method can outperform the same classifier with original features, the wavelet feat res (without fusion), and the wavelet energy features.zh_CN
dc.language.isoen_USzh_CN
dc.subjecthyperspectral datazh_CN
dc.subjectsupport vector machinezh_CN
dc.subjectdiscrete wavelet transformzh_CN
dc.subjectinformation fusionzh_CN
dc.titleFusion of SVMs in Wavelet Domain for Hyperspectral Data Classificationzh_CN
dc.typeArticlezh_CN


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