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Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 8, Iss. 8 — Aug. 1, 2010
  • pp: 811–814

Hyperspectral feature recognition based on kernel PCA and relational perspective map

Hongjun Su and Yehua Sheng  »View Author Affiliations


Chinese Optics Letters, Vol. 8, Issue 8, pp. 811-814 (2010)


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Abstract

A novel joint kernel principal component analysis (PCA) and relational perspective map (RPM) method called KPmapper is proposed for hyperspectral dimensionality reduction and spectral feature recognition. Kernel PCA is used to analyze hyperspectral data so that the major information corresponding to features can be better extracted. RPM is used to visualize hyperspectral data through two-dimensional (2D) maps, and it is an efficient approach to discover regularities and extract information by partitioning the data into pieces and mapping them onto a 2D space. The experimental results prove that the KPmapper algorithm can effectively obtain the intrinsic features in nonlinear high dimensional data. It is useful and impressing for dimensionality reduction and spectral feature recognition.

© 2010 Chinese Optics Letters

OCIS Codes
(100.5010) Image processing : Pattern recognition
(300.6170) Spectroscopy : Spectra
(280.4788) Remote sensing and sensors : Optical sensing and sensors

Citation
Hongjun Su and Yehua Sheng, "Hyperspectral feature recognition based on kernel PCA and relational perspective map," Chin. Opt. Lett. 8, 811-814 (2010)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-8-8-811

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