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Chinese Optics Letters

Chinese Optics Letters


  • 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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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

Hongjun Su and Yehua Sheng, "Hyperspectral feature recognition based on kernel PCA and relational perspective map," Chin. Opt. Lett. 8, 811-814 (2010)

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  1. Y. Du, C.-I. Chang, H. Ren, C.-C. Chang, J. O. Jensen, and F. M. D'Amico, Opt. Eng. 43, 1777 (2004).
  2. C.-I. Chang (ed.), Hyperspectral Data Exploitation: Theory and Applications (Wiley, Hoboken, 2007).
  3. X. Liu, H. Zhao, and N. Li, Acta Opt. Sin. (in Chinese) 3, 844 (2009).
  4. G. F. Hughes, IEEE Trans. Inf. Theory 14, 55 (1968).
  5. H. Su, Y. Sheng, and P. Du, in Proceedings of the ISPRS 7, 279 (2008).
  6. Q. Miao and B. Wang, Chin. Opt. Lett. 6, 104 (2008).
  7. T. V. Bandos, L. Bruzzone, and G. Camps-Valls, IEEE Trans. Geosci. Remote Sens. 47, 862 (2009).
  8. V. Zarzoso and P. Comon, IEEE Trans. Neural Networks 21, 248 (2010).
  9. P. Du, H. Su, and W. Zhang, Proc. SPIE 6752, 675204 (2007).
  10. G. He and L. Peng, Chinese J. Lasers (in Chinese) 36, 2983 (2009).
  11. J. B. Tenenbaum, V. de Silva, and J. C. Langford, Science 290, 2319 (2000).
  12. S. T. Roweis and L. K. Saul, Science 290, 2323 (2000).
  13. B. Scholkopf, A. Smola, and K.-R. Muller, Neural Computation 10, 1299 (1998).
  14. A. Amar, Y. Wang, and G. Leus, IEEE Signal Processing Lett. 17, 473 (2010).
  15. S. Lafon and A. B. Lee, IEEE Trans. Pattern Anal. and Machine Intell. 28, 1393 (2006).
  16. G. Chen and S.-E. Qian, J. Appl. Remote Sens. 1, 013509 (2007).
  17. M. Belkin and P. Niyogi, Neural Computation 15, 1373 (2003).
  18. L. Ma, M. M. Crawford, and J. W. Tian, Electron. Lett. 46, 497 (2010).
  19. C. M. Bachmann, T. L. Ainsworth, and A. R. Fusina, IEEE Trans. Geosci. Remote Sens. 43, 441 (2005).
  20. J. X. Li, Information Visualization 3, 49 (2004).
  21. R. Karbauskait_e, V. Marcinkevi·cus, and G. Dzemyda, Technological and Economic Development of Economy 12, 289 (2006).
  22. S. J. Ga®ey, American Mineralogist 71, 151 (1986).

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