A class-separability-based method for multi/hyperspectral image color visualization

In this paper, a new color visualization technique for multi- and hyperspectral images is proposed. This method is based on a maximization of the perceptual distance between the scene endmembers as well as natural constancy of the resulting images. The stretched CMF principle is used to transform reflectance into values in the CIE L*a*b* colorspace combined with an a priori known segmentation map for separability enhancement between classes. Boundaries are set in the a*b* subspace to balance the natural palette of colors in order to ease interpretation by a human expert. Convincing results on two different images are shown.