A Spatial-Spectral Classification Approach of Multispectral Data for Ground Perspective Materials

A spatial-spectral classification technique for classification of materials within Hyperspectral images is described in this paper. The method considers the influence of neighboring pixels to apply local spatial context features to correctly label an unknown pixel. The spatial and spectral features are jointly applied to a Maximum Likelihood classifier that uses material class models defined by a Mixture of Gaussians to adaptively account for spectral variability and noise. Experimental results compare the application of spatial and spectral features with only spectral features on the classification of materials common to scenes viewed from the ground perspective.