激光与光电子学进展, 2015, 52 (4): 041102, 网络出版: 2015-04-02   

基于局部学习的玉米种子近红外高光谱图像鉴选 下载: 505次

Discrimination of Maize Seeds by Near Infrared Ray Hyperspectral Imaging with Local Learning
作者单位
江南大学轻工业过程先进控制教育部重点实验室, 江苏 无锡 214122
摘要
将局部学习算法引入到种子的近红外高光谱图像最优波段选择中,并建立偏最小二乘判别分析分类预测模型,实现少波段条件下的玉米种子的快速鉴选。实验共采集了6 类样本共720 粒的玉米种子在874~1734 nm 波段范围内的256 幅近红外高光谱图像,利用局部学习算法获得波段的特征权重,并依据特征权重选择了最优波段。实验结果表明局部学习算法可有效获取最优鉴选波段,在13 个最优波段条件下,对6 组玉米种子可以获得平均纯度为95.97%的鉴选结果,为实现玉米种子的快速鉴选提供了一个合适的技术途径。
Abstract
The local learning algorithm is introduced into the optimal wavelength selection of near infrared ray hyperspectral imaging of maize seeds. These obtained wavelengths are used to develop a discrimination model coupled with partial least squares discriminant analysis to implement the rapid discrimination of maize seeds using less wavelengths. 256 near infrared ray hyperspectral images between 874~1734 nm wavelengths are acquired using a hyperspectral imaging system for 720 maize seed samples including six varieties. Local learning algorithm is proposed to calculate the weight values of wavelengths, and the optimal wavelengths are selected according to the weight values. The experimental results show that local learning algorithm can effectively select the optimal wavelengths. Using 13 optimal wavelengths, six groups of maize seeds achieve an average purity of 95.97%, which can provide a suitable technical way for the rapid discrimination of maize seeds.

唐金亚, 黄敏, 朱启兵. 基于局部学习的玉米种子近红外高光谱图像鉴选[J]. 激光与光电子学进展, 2015, 52(4): 041102. Tang Jinya, Huang Min, Zhu Qibing. Discrimination of Maize Seeds by Near Infrared Ray Hyperspectral Imaging with Local Learning[J]. Laser & Optoelectronics Progress, 2015, 52(4): 041102.

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