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Chinese Optics Letters

Chinese Optics Letters

| PUBLISHED MONTHLY BY CHINESE LASER PRESS AND DISTRIBUTED BY OSA

  • Vol. 9, Iss. 1 — Jan. 10, 2011
  • pp: 011003–

Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classif ication

Kun Tan and Peijun Du  »View Author Affiliations


Chinese Optics Letters, Vol. 9, Issue 1, pp. 011003- (2011)


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Abstract

Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer II hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four principal components from principal component analysis and wavelet texture provides the highest accuracy (97.06%). Multi-kernel SVM is therefore an effective approach to improve the accuracy of hyperspectral image classification and to expand possibilities for remote sensing image interpretation and application.

© 2011 Chinese Optics Letters

OCIS Codes
(100.5010) Image processing : Pattern recognition
(100.7410) Image processing : Wavelets
(100.4145) Image processing : Motion, hyperspectral image processing

Citation
Kun Tan and Peijun Du, "Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classif ication," Chin. Opt. Lett. 9, 011003- (2011)
http://www.opticsinfobase.org/col/abstract.cfm?URI=col-9-1-011003


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