论文标题
使用高斯贝叶斯模型和神经网络分类的农作物类型，从NASA高光谱卫星图像中的Ghisaconus USGS数据进行分类
Classifying Crop Types using Gaussian Bayesian Models and Neural Networks on GHISACONUS USGS data from NASA Hyperspectral Satellite Imagery
论文作者
论文摘要
高光谱想象是一种数字成像，每个像素通常包含数百个波长的光，从而提供有关像素中存在的材料的光谱信息。在本文中，我们提供了分类方法，用于确定USGS Ghisaconus数据中的作物类型，该方法包含来自NASA Hyperion Satellite收集的美国五种主要美国农作物（冬小麦，大米，玉米，大豆，大豆，大豆和棉花）的大约7,000个像素光谱，并包括光谱，地球，化作物，阶段，阶段，阶段，阶段和阶段。我们采用标准LDA和QDA以及计算农作物类型和阶段的联合概率，然后是作物类型的边际概率的贝叶斯自定义版本，表现优于非氨基植物方法。我们还测试了一个单层神经网络，该神经网络的数据与LDA和QDA相当，但不如贝叶斯方法可比。
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.
