Subpixel mapping of hyperspectral data using high resolution color images

This article introduces a method that uses information from a high spatial resolution color image to perform subpixel mapping of a lower spatial resolution hyperspectral image. The method uses a modified majority voting approach, that makes a distinction between pure and mixed pixels, in order to combine a classification map of the hyperspectral data with a segmentation map of the color image. Experiments, conducted on two hyperspectral images, show excellent results, when compared to conventional classification of the hyperspectral data only. The developed classification scheme results in much higher accuracies and leads to large visual improvements, including well-defined class transitions and fairly homogeneous objects.