高光谱技术在血迹分类识别中的应用
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摘要
利用高光谱技术对血迹种类进行无损识别研究。采用小波变换技术对400～950 nm之间的原始光谱进行去噪处理,并对处理后的光谱进行特征波段选择,建立全波段和特征波长下的血迹种类识别模型。结果表明,利用特征波长与支持向量机(SVM)结合建立的血迹种类识别模型的识别准确率及识别时间分别为98%和0.2 s,优于全波段建立的模型。研究表明,采用高光谱技术对血迹种类识别是可行的。
Nondestructive identification of blood type is studied by hyperspectral technology in this paper. The original spectrum between 400-950 nm is denoised by wavelet transform, and then the characteristic spectrum of the processed spectrum is selected, finally the bloodstain type recognition models in the whole band and characteristic wavelengths are established. The results show that the model established in the characteristic wavelength combined with Support Vector Machine(SVM) can realize recognition accuracy of 98% and recognition time of 0.2 s, which is better than that of the model in full band. Research indicates that the hyperspectral technology is feasible for identifying the bloodstain types.
Nondestructive identification of blood type is studied by hyperspectral technology in this paper. The original spectrum between 400-950 nm is denoised by wavelet transform, and then the characteristic spectrum of the processed spectrum is selected, finally the bloodstain type recognition models in the whole band and characteristic wavelengths are established. The results show that the model established in the characteristic wavelength combined with Support Vector Machine(SVM) can realize recognition accuracy of 98% and recognition time of 0.2 s, which is better than that of the model in full band. Research indicates that the hyperspectral technology is feasible for identifying the bloodstain types.
引文
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[11]毛国庆,滕奇志,吴拥,等.基于BP神经网络的剩余油形态识别[J].太赫兹科学与电子信息学报,2014,12(6):858-864.(MAO Guoqing,TENG Qizhi,WU Yong,et al.Shape recognition of remained oil based on BP neural network[J].Journal of Terahertz Science and Electronic Information Technology,2014,12(6):858-864.)DOI:10.11805/TKYDA201406.0858.
[12]LIU Yufei,PI Dechang,CHENG Qiyou,et al.Ensemble kernel method:SVM classification based on game theory[J].Journal of Systems Engineering and Electronics,2016,27(1):251-259.DOI:10.1109/JSEE.2016.00025.
[2]ZHANG X,LIU F,HE Y,et al.Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging[J].Biosystems Engineering,2013,115(1):56-65.DOI:10.1016/j.biosystemseng.2013.02.007.
[3]李岚涛,汪善勤,任涛,等.基于高光谱的冬油菜叶片磷含量诊断模型[J].农业工程学报,2016,32(14):209-218.(LILantao,WANG Shanqin,REN Tao,et al.Evaluating models of leaf phosphorus content of winter oilseed rape based on hyperspectral data[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(14):209-218.)DOI:10.11975/j.issn.1002-6819.2016.14.028.
[4]赵明富,刘自迪,邹雪,等.基于高光谱成像技术对马铃薯外部缺陷的识别[J].激光杂志,2016(3):20-24.(ZHAO Mingfu,LIU Zidi,ZOU Xue,et al.Detection of defects on potatoes by hyperspectral imaging technology[J].Laser Journal,2016(3):20-24.)DOI:10.14016/j.cnki.jgzz.2016.03.020.
[5]余伟,彭宽宽,陈伟,等.基于高光谱技术的培养基上细菌菌落分类方法研究[J].分析化学,2016(8):1221-1226.(YUWei,PENG Kuankuan,CHEN Wei,et al.Classification of bacterial colonies on agar plates using hyperspectral imaging technology[J].Chinese Journal of Analytical Chemistry,2016(8):1221-1226.)DOI:10.11895/j.issn.0253-3820.160053.
[6]赵明富,冯小平,张政委,等.基于光谱分析技术的不同介质血迹陈旧度研究[J].压电与声光,2013,35(6):806-809.(ZHAO Mingfu,FENG Xiaoping,ZHANG Zhengwei,et al.Study on detection of bloodstain age in different objects based on spectroscopy technology[J].Piezoelectrics&Acoustooptics,2013,35(6):806-809.)DOI:10.3969/j.issn.1004-2474.2013.06.010.
[7]董永芳,孟耀勇,张平丽,等.基于可见反射光谱和遗传区间偏最小二乘法的血迹年龄预测研究[J].光学学报,2015,35(8):369-374.(DONG Yongfang,MENG Yaoyong,ZHANG Pingli,et al.Accurate age estimation of bloodstains based on visible reflectance and genetic algorithm interval partial Least Squares[J].Acta Optica Sinica,2015,35(8):369-374.)DOI:10.3788/aos201535.0830001.
[8]万雄,王建,刘鹏希,等.近红外透射光谱的动物全血鉴别[J].光谱学与光谱分析,2016,36(1):80-83.(WAN Xiong,WANGJian,LIU Pengxi,et al.Identification of animal whole blood based on near infrared transmission spectroscopy[J].Spectroscopy and Spectral Analysis,2016,36(1):80-83.)DOI:10.3964/j.issn.1000-0593(2016)01-0080-04.
[9]赵明富,夏曦,张政委,等.基于高光谱图像技术的指纹识别研究[J].激光杂志,2013,34(1):45-47.(ZHAO Mingfu,XIAXi,ZHANG Zhengwei,et al.Study on detection of fingerprint recognition based on hyperspectral imaging technology[J].Laser Journal,2013,34(1):45-47.)DOI:10.3969/j.issn.0253-2743.2013.01.022.
[10]张胤鸣.基于FTIR光谱技术鉴别血痕种属及形成时间的实验研究[D].西安:西安交通大学,2017.(ZHANG Yinming.The estimation of time since deposition and species of bloodstains based on FTIR spectroscopy[D].Xi’an,China:Xi’an Jiaotong University,2017.)
[11]毛国庆,滕奇志,吴拥,等.基于BP神经网络的剩余油形态识别[J].太赫兹科学与电子信息学报,2014,12(6):858-864.(MAO Guoqing,TENG Qizhi,WU Yong,et al.Shape recognition of remained oil based on BP neural network[J].Journal of Terahertz Science and Electronic Information Technology,2014,12(6):858-864.)DOI:10.11805/TKYDA201406.0858.
[12]LIU Yufei,PI Dechang,CHENG Qiyou,et al.Ensemble kernel method:SVM classification based on game theory[J].Journal of Systems Engineering and Electronics,2016,27(1):251-259.DOI:10.1109/JSEE.2016.00025.