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Proceedings Paper

Hyperspectral ratio feature selection: agricultural product inspection example
Author(s): Songyot Nakariyakul; David Casasent
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Paper Abstract

We describe a fast method for dimensionality reduction and feature selection of ratio features for classification in hyperspectral data. The case study chosen is to discriminate internally damaged almond nuts from normal ones. For this case study, we find that using the ratios of the responses in several wavebands provides better features than a subset of waveband responses. We find that use of the Euclidean Minimum Distance metric gives slightly better results than the more conventional Spectral Angle Mapper distance metric in a nearest neighbor classifier.

Paper Details

Date Published: 19 November 2004
PDF: 11 pages
Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); doi: 10.1117/12.568237
Show Author Affiliations
Songyot Nakariyakul, Carnegie Mellon Univ. (United States)
David Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 5587:
Nondestructive Sensing for Food Safety, Quality, and Natural Resources
Yud-Ren Chen; Shu-I Tu, Editor(s)

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