Abstract

Methods for correcting morphological-based deficiencies in hyperspectral images of round objects

Ron Haff,^a,* Sirinnapa Saranwong^b,c and Sumio Kawano^b,d
aUnited States Department of Agriculture, Agricultural Research Service, Western Regional Research Centre, Albany, CA, 95616, USA. E-mail: Ron.Haff@ars.usda.gov
^bNational Food Research Institute, National Agriculture and Food Research Organization, Tsukuba, 305-8642, Japan
^cPresent address: Bruker Optics KK, Tokyo, 104-0033, Japan
^dPresent address: Faculty of Agriculture, Kagoshima University, Kagoshima 890-0065, Japan

Hyperspectral images of curved surfaces contain undesirable artefacts that are a consequence of the morphology, or shape of the sample. A software correction was developed to remove the variation in pixel intensity in hyperspectral images of spherical samples generated on a linescan type imaging system. The correction is based directly on well known physical effects involving light reflection and intensity. The three predominant principles investigated are the behaviour of light reflected from Lambertian surfaces, the 1/x² relationship between light intensity and distance from the source, and the variation in arc length along a circle as seen from the detectors. The algorithm was tested using hyperspectral images of a uniform spherical Teflon sample. Pixel intensity profiles and histograms were generated for the corrected images and evaluated to determine the effectiveness of the algorithm based on the fact that the ideal result would be a uniform image (as is appropriate for a uniform sample). Results indicate that the algorithm effectively improves pixel intensity uniformity, although some variability remains. Contributing factors to the remaining pixel intensity variation in the corrected images include non-uniformity of sample illumination, specular reflection, unintended ambient light and reflections from surfaces. The same principle can be applied to samples with circular cross sections along a particular axis, which includes many agricultural commodities.

Keywords: hyperspectral, linescan, morphology, correction, algorithm

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