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首页> 外文会议>Conference on Biological Quality and Precision Agriculture II, Nov 6-8, 2000, Boston, USA >Hyperspectral Data Discrimination Methods
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Hyperspectral Data Discrimination Methods

机译：高光谱数据鉴别方法

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摘要

Hyperspectral data provides spectral response information that provides detailed chemical, moisture, and other descriptions of constituent parts of an item. These new sensor data are useful in USDA product inspection. However, such data introduce problems such as the curse of dimensionality, the need to reduce the number of features used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). Several two-step methods are compared to a new and preferable single-step spectral decomposition algorithm. Initial results on hyperspectral data for good/bad almonds and for good/bad (aflatoxin infested) com kernels are presented. The hyperspectral application addressed differs greatly from prior USDA work (PLS) in which the level of a specific channel constituent in food was estimated. A validation set (separate from the test set) is used in selecting algorithm parameters. Threshold parameters are varied to select the best P_c operating point. Initial results show that nonlinear features yield improved performance.
机译：高光谱数据提供光谱响应信息，该信息提供详细的化学成分，水分和其他有关项目组成部分的描述。这些新的传感器数据在USDA产品检查中很有用。但是，此类数据会带来一些问题，例如维数的诅咒，需要减少用于容纳实际小训练集大小的特征的数量以及需要使用区分性特征并仍实现良好的概括性（可比训练和测试集性能） 。将几种两步法与一种新的且优选的单步谱分解算法进行了比较。给出了有关好/坏杏仁和好/坏（黄曲霉毒素侵染）玉米仁的高光谱数据的初步结果。所解决的高光谱应用与先前的USDA工作（PLS）有很大不同，在该工作中，对食品中特定通道成分的水平进行了估算。验证集（与测试集分开）用于选择算法参数。改变阈值参数以选择最佳的P_c工作点。初步结果表明，非线性特征可提高性能。

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