Imaging Components, Systems, and Processing

Classification of bee pollen grains using hyperspectral microscopy imaging and Fisher linear classifier

[+] Author Affiliations
Kang Su, Siqi Zhu, Lin Wei, Zhen Li, Hao Yin, Pingping Ye, Anming Li, Zhenqiang Chen

Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou 510632, China

Jinan University, Institute of Optoelectronic Engineering, College of Science and Technology, No. 601, Huangpu Road West, Guangzhou 510632, China

Migao Li

Guangzhou Liss Optical Instrument Co., Ltd., No. 81 Taojin Bei Road, Guangzhou 510095, China

Opt. Eng. 55(5), 053102 (May 05, 2016). doi:10.1117/1.OE.55.5.053102
History: Received November 13, 2015; Accepted April 8, 2016
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Abstract.  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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Kang Su ; Siqi Zhu ; Lin Wei ; Zhen Li ; Hao Yin, et al.
"Classification of bee pollen grains using hyperspectral microscopy imaging and Fisher linear classifier", Opt. Eng. 55(5), 053102 (May 05, 2016). ; http://dx.doi.org/10.1117/1.OE.55.5.053102


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