  Hyperspectral Imagining is a type of digital imaging in which each pixel
contains typically hundreds of wavelengths of light providing spectroscopic
information about the materials present in the pixel. In this paper we provide
classification methods for determining crop type in the USGS GHISACONUS data,
which contains around 7,000 pixel spectra from the five major U.S. agricultural
crops (winter wheat, rice, corn, soybeans, and cotton) collected by the NASA
Hyperion satellite, and includes the spectrum, geolocation, crop type, and
stage of growth for each pixel. We apply standard LDA and QDA as well as
Bayesian custom versions that compute the joint probability of crop type and
stage, and then the marginal probability for crop type, outperforming the
non-Bayesian methods. We also test a single layer neural network with dropout
on the data, which performs comparable to LDA and QDA but not as well as the
Bayesian methods.
