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Proceedings Paper

Hyperspectral data discrimination methods
Author(s): David P. Casasent; Xuewen Chen
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Paper Abstract

Hyperspectral data provides spectral response information that provides detailed chemical, moisture, and other description 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) corn 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 Pc operating point. Initial results show that nonlinear features yield improved performance.

Paper Details

Date Published: 29 December 2000
PDF: 10 pages
Proc. SPIE 4203, Biological Quality and Precision Agriculture II, (29 December 2000); doi: 10.1117/12.411754
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Xuewen Chen, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 4203:
Biological Quality and Precision Agriculture II
James A. DeShazer; George E. Meyer, Editor(s)

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