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.
翻译:超光谱想象是一种数字成像,其中每个像素通常含有数百个波长的光谱,提供关于像素中所含材料的光谱信息。在本文中,我们提供了美国地质科学研究所GHISACONUS数据中用于确定作物类型的分类方法,该数据包含美国航天局超音卫星收集的五大美国农作物(小麦、大米、玉米、大豆、大豆和棉花)的大约7 000个象素光谱,其中包括每个像素的频谱、地理定位、作物类型和生长阶段。我们采用了标准的LDA和QDA以及巴耶斯定制版本,这些版本计算了作物类型和阶段的共同概率,然后是作物类型的边际概率,比非巴耶斯方法差。我们还测试了一个单层神经网络,其数据与LDA和QDA相仿,但与Bayesian方法不同。