I. Introduction
HYPERSPECTRAL data contain a set of images with the same geographic scene. These images correspond to different spectral bands of electromagnetic radiation. Fixed a band, the hyperspectral data reduce to a single image containing the scene structure information of different materials. Fixed an image coordinate, it obtains a spectral curve vector, which is called a pixel [here, “pixel” refers to “sample” in a hyperspectral image (HSI)]. Different materials have different absorptions or reflections at a certain spectral band. Thus, it can identify and classify the materials based on their spectral curves. Traditional classifiers, such as the Bayesian classifier, the -nearest neighbor classifier, and neural networks, use the spectral signatures in the HSI classification.