UM
Dimension reduction with randomized anisotropic transform for hyperspectral image classification
Huiwu Luo; Lina Yang; Haoliang Yuan; Yuan Yan Tang
2013-12-06
Conference Name2013 IEEE International Conference on Cybernetics (CYBCO)
Source Publication2013 IEEE International Conference on Cybernetics, CYBCONF 2013
Pages156-161
Conference Date13-15 June 2013
Conference PlaceLausanne, Switzerland
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Dimension reduction plays an important role in the community of high dimensional data analysis. The notion of random anisotropic transform (RAT), which was applied to speed up the computation procedure of dimension reduction kernel (DRK) with Isomap embedding (Isomap-RAT), was introduced in this paper. Nevertheless, traditional Isomap-RAT does not consider the intrinsic dimension that the hyperspectral image data resides on. Moreover, The DRK of Isomap embedding is not always guaranteed to be positive semi-definite. Thus, this paper proposed a kernel Isomap-Hysime random anisotropic transform (KIH-RAT) to deal with these challenges that met frequently in reality. The proposed methodology consists of two main terms: 1) a kernel term that finds an approximative constant which is added to the dissimilar matrix to make the DRK to be positive semi-definite; and 2) an intrinsic dimension assessment term that employs Hysime to estimate the intrinsic dimension of hyperspectral image data to preserve the geometries of original information as much as possible. The proposed method is exhaustively tested on two reduced feature spaces that relate to the classification of real hyperspectral remote sensing images. The effectiveness and feasibility of presented KIH-RAT methodology are illustrated by the experiment results from both real hyperspectral image examples.

KeywordAnistropic Transform Dimension Reduction Hyperspectral Image Random Projection Randomized Anisotropic Transform
DOIhttps://doi.org/10.1109/CYBConf.2013.6617465
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics
WOS IDWOS:000340924600027
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
AffiliationDepartment of Computer and Information Science, University of Macau 999078, Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Huiwu Luo,Lina Yang,Haoliang Yuan,et al. Dimension reduction with randomized anisotropic transform for hyperspectral image classification[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2013:156-161.
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