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
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