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

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.