5 May 2016 Classification of bee pollen grains using hyperspectral microscopy imaging and Fisher linear classifier
Author Affiliations +
Optical Engineering, 55(5), 053102 (2016). doi:10.1117/1.OE.55.5.053102
The rapid and accurate classification of bee pollen grains is still a challenge. The purpose of this paper is to develop a method which could directly classify bee pollen grains based on fluorescence spectra. Bee pollen grain samples of six species were excited by a 409-nm laser diode source, and their fluorescence images were acquired by a hyperspectral microscopy imaging (HMI) system. One hundred pixels in the region of interest were randomly selected from each single bee pollen species. The fluorescence spectral information in all the selected pixels was stored in an n-dimensional hyperspectral data set, where n=37 for a total of 37 hyperspectral bands (465 to 645 nm). The hyperspectral data set was classified using a Fisher linear classifier. The performance of the Fisher linear classifier was measured by the leave-one-out cross-validation method, which yielded an overall accuracy of 89.2%. Finally, additional blinded samples were used to evaluate the established classification model, which demonstrated that bee pollen mixtures could be classified efficiently with the HMI system.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Kang Su, Siqi Zhu, Lin Wei, Zhen Li, Hao Yin, Pingping Ye, Anming Li, Zhenqiang Chen, Migao Li, "Classification of bee pollen grains using hyperspectral microscopy imaging and Fisher linear classifier," Optical Engineering 55(5), 053102 (5 May 2016). https://doi.org/10.1117/1.OE.55.5.053102



Hyperspectral imaging

Classification systems

Imaging systems

Image classification

Image segmentation

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