食品科学 ›› 2017, Vol. 38 ›› Issue (20): 292-299.doi: 10.7506/spkx1002-6630-201720043

• 安全检测 • 上一篇    下一篇

基于信息熵的枸杞分级高光谱图像特征波长选择方法

于慧春,王润博,殷勇,刘云宏   

  1. (河南科技大学食品与生物工程学院,河南?洛阳 471023)
  • 出版日期:2017-10-25 发布日期:2017-09-29
  • 基金资助:
    河南省科技攻关项目(172102210256;172102310617)

Wavelength Selection of Hyperspectral Image Analysis for Wolfberry Grading Based on Information Entropy

YU Huichun, WANG Runbo, YIN Yong, LIU Yunhong   

  1. (College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471023, China)
  • Online:2017-10-25 Published:2017-09-29

摘要: 为获得适合枸杞分级的最佳高光谱特征波长图像,实验提出一种基于信息熵的高光谱图像特征波长选择方法。通过计算在不同波长条件下每一个枸杞样本的自信息,得到每一类枸杞高光谱图像的平均自信息;通过计算对应任意2?个不同类别的枸杞样本的互信息,得到任意2?类枸杞高光谱图像的平均互信息。最终获得枸杞高光谱图像在某一波长条件下的平均互信息与各自平均自信息和的比值,定义为A。A值可以作为枸杞分级高光谱图像特征波长选择的量化指标。结果显示,枸杞分级的最优波长为950?nm。最后,提取特定波长条件下所有枸杞图像的纹理特征,并采用Fisher判别分析对6?类枸杞进行分类验证。基于信息熵的枸杞分级高光谱图像特征波长选择方法是可行的。

关键词: 高光谱图像, 信息熵, 特征波长, 枸杞, 分级

Abstract: In order to obtain the best hyperspectral characteristic wavelength for wolfberry grading, a feature wavelength selection method for hyperspectral image analysis based on information entropy was presented. Under different wavelengths, firstly, the self-information of each sample image was calculated, and the mean self-information of hyperspectral images of each class of wolfberry was calculated; secondly, the mutual information between two arbitrary sample classes was calculated to obtain the mean mutual information between the corresponding hyperspectral images. Furthermore, the ratio of the mean mutual information to the sum of the mean self-information which corresponded to each sample class under a certain wavelength was calculated and defined as A. Finally,?it was found that A value could be taken as a quantitative index to select the optimal hyperspectral image wavelength for wolfberry grading. The analytical results showed that the optimal wavelength was 950 nm. Then the texture features of all wolfberry samples under the selected wavelength were extracted, and Fisher discriminant analysis (FDA) was employed to classify six classes of wolfberries for the purpose of verification. The results of this study showed that wavelength selection of hyperspectral image analysis based on information entropy is highly feasible for wolfberry grading.

Key words: hyperspectral image, information entropy, optimal wavelength, wolfberry, classification

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