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Hyperspectral Imaging and Feature Extraction Methods in Fruit and Vegetable Defect Inspection

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  033119,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.15013) @2003
Authors:   Xuemei Cheng, Yud Ren Chen, Yang Tao, Diane Chan, Chien Yi Wang
Keywords:   Feature extraction, hyperspectral imaging, hyperspectral sensing, dimensionality reduction, classification, principal component, minor component, PCA, FLD

High-resolution hyperspectral imaging (HSI) provides an abundance of spectral data for feature analysis in image processing. Usually, the amount of information contained in hyperspectral images is excessive and redundant, and data mining in waveband selection is needed. In applications for fruit and vegetable damage inspection, effective spectral combination and data fusing methods are required in order to select a few optimal wavelengths without losing key information in the original HSI data. In this paper, we present a new method that combines the principal component analysis (PCA) and fishers linear discriminant (FLD) method in a way that maximizes the representation and classification effects on the extracted new feature bands. The method is applied to the detection of chilling injury on cucumbers. Compared with PCA and FLD when used separately, this new integrated PCA-FLD method has achieved results showing better classification performance when tested on different types of samples. This method is ready to be extended to other hyperspectral imaging applications for fruit and vegetable safety and quality inspections.

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