Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

摘要

Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data.Features extracted using KPCA are classified using linear support vector machines. In one experiment, it is shown that kernelprincipal component features are more linearly separable than features extracted with conventional principal componentanalysis. In a second experiment, kernel principal components are used to construct the extended morphological profile (EMP).Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principalcomponent analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCAfor the analysis of hyperspectral data. For the one data set, the overall classification accuracy increases from 79% to 96% with theproposed approach.