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Spatial smoothing over the original hyperspectral data based on wavelet and anisotropic partial differential equations is incorporated using composite kernel in graph-based classifiers. The kernels combine spectral-spatial relationships using the smoothed and original hyperspectral images. Experiments with different real hyperspectral scenarios are presented. Comparison with recent graph-based methods shows that the proposed scheme gives better classification with lower computational cost.