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Correction of Curvature-Induced Spectral Variability in Hyperspectral Images of Wheat Kernels

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

Citation:  Paper number  053070,  2005 ASAE Annual Meeting . (doi: 10.13031/2013.19826) @2005
Authors:   W. Wang, J. Paliwal
Keywords:   Hyperspectral imaging, curvature correction, ellipsoidal surface fitting, morphological shrinking

The paper addresses a practical problem associated with using hyperspectral imaging technique for whole wheat assessment. The curved surface of wheat kernels introduces spectral variability among spectral data from different parts of the kernels. Two functions viz. simulated ellipsoidal surface fitting and morphological shrinking algorithm, were used to compensate for reflectance variation caused by the curved surface of wheat kernels. Kernels of four different western Canadian wheat classes were tested and results showed that morphological shrinking method was effective in reducing the overall spectral variability. It was also found that ellipsoidal surface fitting was as effective as morphological shrinking method along the minor axis direction of wheat kernel surface but failed to reduce variation along the major axis. The coefficients of variation (CV) values were reduced to approximately half of the original values along the minor axis direction within the 1100 nm - 1350 nm spectral region after applying both methods.

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