Improving Hyperspectral Image Classification based on Graphs using Spatial Preprocessing

Spatial smoothing over the original hyperspectral data based on wavelet and partial differential equations (PDEs) are incorporated in the classifiers using composite kernel with kNN graphs. The kernels combine spectral-spatial relationships using the smoothed and original images. Experiments with real hyperspectral scenarios are presented. Comparison with recent graph based methods show that the proposed scheme improves existing methods.