Improving Hyperspectral Image Classification Using Spatial Preprocessing

Santiago Velasco-Forero

doi:10.1109/LGRS.2009.2012443

Improving Hyperspectral Image Classification Using Spatial Preprocessing

2009-04-01

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Santiago Velasco-Forero *

V

Vidya Manian

DOI：10.1109/LGRS.2009.2012443

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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.

Keywords:

Graph classification

hyperspectral images

semisupervised learning

Improving Hyperspectral Image Classification Using Spatial Preprocessing

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