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

Classifying Crop Types using Gaussian Bayesian Models and Neural Networks on GHISACONUS USGS data from NASA Hyperspectral Satellite Imagery