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A NOVEL INTEGRATED PCA AND FLD METHOD ON HYPERSPECTRAL IMAGE FEATURE EXTRACTION FOR CUCUMBER CHILLING DAMAGE INSPECTION
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: Transactions of the ASAE. Vol. 47(4): 1313-1320 . (doi: 10.13031/2013.16565) @2004
Authors: X. Cheng, Y. R. Chen, Y. Tao, C. Y. Wang, M. S. Kim, A. M. Lefcourt
Keywords: Classification, Dimensionality reduction, Feature extraction, FLD, Hyperspectral imaging, Hyperspectral sensing, PCA, Principal component
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 for waveband selection is needed. In applications such as fruit and vegetable defect inspections, effective spectral combination and data fusing methods are required in order to select a few optimal wavelengths without losing the crucial information in the original hyperspectral data. In this article, we present a novel method that combines principal component analysis (PCA) and Fisher’s linear discriminant (FLD) method to show that the hybrid PCA-FLD method maximizes the representation and classification effects on the extracted new feature bands. The method is applied to the detection of chilling injury on cucumbers. Based on tests on different types of samples, results show that this new integrated PCA-FLD method outperforms the PCA and FLD methods when they are used separately for classifications. This method adds a new tool for the multivariate analysis of hyperspectral images and can be extended to other hyperspectral imaging applications for fruit and vegetable safety and quality inspections.(Download PDF) (Export to EndNotes)