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基于连续投影算法-遗传算法-BP神经网络的可见/近红外光谱木材识别
doi: 10.11833/j.issn.2095-0756.20210377
Visible/near infrared spectrum wood identification based on SPA-GA-BP neural network
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摘要:
目的 基于可见/近红外光谱技术，以10种木材为研究对象，探索不同预处理和特征提取方法下BP神经网络识别木材的效果。 方法 利用美国ASD公司生产的LabSpec 5000光谱仪采集10种木材的光谱图，分别进行移动平均法处理、移动平均法+多元散射校正(MSC)、移动平均法＋标准正态变量变换(SNV)、Savitzky-Golay卷积平滑算法(S-G滤波器)、S-G滤波器+MSC和S-G滤波器+SNV的预处理，运用主成分分析法(PCA)、连续投影算法(SPA)、SPA和遗传算法(GA)联合分别进行特征提取，将提取的特征结合BP神经网络进行木材识别试验。 结果 以SPA和GA联合提取光谱特征时，移动平均法+SNV的预处理效果最佳，以吸收峰为起始波段(Winitial=1 445 nm)、吸收峰个数为特征个数(Ntot=9)时，识别率较高，特征个数大部分减少为SPA提取特征值个数的一半左右。BP神经网络的平均识别速度提升明显。10种木材的平均识别率为98.0%，其中7种木材的识别率达到了100.0%。 结论 在移动平均法+SNV的预处理下，SPA和GA联合提取光谱图的特征，既可提高BP神经网络识别木材的正确率，又可提升识别速度。图3表6参23 Abstract:Objective The purpose of this study is to explore the effect of BP neural network identification under different pretreatment and feature extraction methods based on visible/near infrared spectroscopy technology, with 10 wood species as objects. Method The LabSpec 5000 spectrometer produced by American ASD company was used to collect the spectrograms of 10 species of wood, which were pretreated by moving average method, moving average method + multiplicative scattering correction(MSC), average method+standard normal variable transformation (SNV), Savitzky-Golay convolution smoothing algorithm (S-G filter), S-G filter+MSC and S-G filter+SNV. Meanwhile, principal component analysis(PCA), successive projections algorithm(SPA), and SPA combined with genetic algorithm(GA) were used for feature extraction respectively. The extracted features were combined with BP neural network for wood identification test. Result When SPA and GA were combined to extract spectral features, the moving average+SNV method had the best preprocessing effect. When absorption peak was used as the initial waveband (Winitial=1 445 nm) and the number of absorption peaks (Ntot=9) as the number of features, the identification rate was high, and the number of features mostly decreased to about 1/2 of the number of feature values extracted by SPA. The average identification speed of BP neural network significantly increased. The average identification rate of the 10 wood species was 98.0%, and the identification rate of 7 of them reached 100.0%. Conclusion Under the pretreatment of moving average method+SNV, the combined use of SPA and GA in spectral feature extraction can improve not only the accuracy of wood identification by BP neural network, but also the identification speed. [Ch, 3 fig. 6 tab. 23 ref.] -
表 1 不同预处理的PCA-BP神经网络识别率
Table 1. PCA-BP neural network recognition with different preprocessing
检测方式 预处理方法 累计贡
献率/%主成分
个数/个平均识
别率/%可见/近红外光谱 对照组 95 12 80.2 移动平均法 95 14 81.4 移动平均法+MSC 95 10 82.1 移动平均法+SNV 95 11 83.5 S-G滤波器 95 12 81.3 S-G滤波器+MSC 95 13 82.9 S-G滤波器+SNV 95 15 84.7 
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表 2 不同预处理的SPA-BP神经网络平均识别率
Table 2. Average recognition rate of SPA-BP neural network with different pretreatments
预处理方法 平均识
别率/%预处理方法 平均识
别率/%对照组 86.1 S-G滤波器 86.4 移动平均法 87.2 S-G滤波器+MSC 86.8 移动平均法+MSC 86.5 S-G滤波器+SNV 87.3 移动平均法+SNVSNV 88.2
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表 3 10种木材吸收峰个数和集中波段
Table 3. Number of absorption peaks and concentrated bands of 10 kinds of wood
木材种类 吸收峰个数/个 集中分布波段/nm 红檀 7 920~970、1 010~1 060、1 210~1 260、1 570~1 620、1 779~1 829、1 921~1 971、2 122~2 172 大果紫檀 7 930~980、1 020~1 070、1 220~1 270、1 580~1 630、1 780~1 830、1 920~1 970、2 120~2 170 檀香紫檀 7 932~982、1 023~1 073、1 221~1 271、1 568~1 618、1 777~1 827、1 921~1 971、2 123~2 173 刺猬紫檀 9 763~813、1 222~1 272、1 308~1 358、1 461~1 511、1 548~1 598、1 760~1 810、1 931~1 981、
2 092~2 142、2 211~2 261巴里黄檀 9 765~815、1 221~1 271、1 307~1 357、1 466~1 516、1 545~1 595、1 769~1 819、1 930~1 980、
2 087~2 137、2 219~2 269红檀香 9 753~803、1 223~1 273、1 309~1 359、1 463~1 513、1 558~1 608、1 771~1 821、1 932~1 982、
2 092~2 142、2 212~2 262破布木 9 763~813、1 222~1 272、1 317~1 367、1 463~1 513、1 551~1 601、1 772~1 822、1 933~1 983、
2097~2147、2214~2264木犀科豆瓣香 9 766~816、1230~1280、1317~1367、1468~1518、1554~1604、1775~1825、1940~1990、
2095~2145、2 216~2 266中美洲黄檀 9 753~803、1 218~1 268、1 305~1 355、1 457~1 507、1 544~1 594、1 769~1 819、1 928~1 978、
2 084~2 134、2 209~2 259黑檀 9 881~931、1 218~1 268、1 305~1 355、1 452~1 502、1 557~1 607、1 772~1 822、1 923~1 973、
2 092~2 142、2 218~2 268
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表 4 不同起始波段的SPA-BP神经网络平均识别率
Table 4. Average recognition rate of SPA-BP neural network with different starting bands
特征值数/个 起始波段/nm 10种木材提取特征波长分布/nm 平均识别率/% 10 895 364~368、2 141~2 144；402~410；418~426；324、2 135~2 142；375~383；432~440；400~408；476~484；420~428；1 452~1 460 89.7 10 1 445 478~586；410~418；423~431；500~508；405~413；436~444；418~426；693~701；891~899；888~896 90.4 10 1 605 133~135、2 137~2 142；891~899；891~899；2 135~2 142、2 132；419~427；819~827；420~428；446~454；892~990；893~901 90.1 10 15 2133~135、2 137~2 142；2 133~2 135、2 137~2 142；408~416；292、22 135~2 142；375~383；430~438；414~422；461~469；420~428；890~898 88.3 10 795 61~64、2 139~2 143；405~413；420~428；326、2 135~2 142；378~386；527~
535；403~411；478~486；420~422、1 453~1 458；1 350~1 35889.5 10 995 203~209、2 141~2 142；399~407；418~426；349~352、2 138~2 142；3381~389；434~442；421~429；485~493；527~535；1 452、1 454~ 1458、1 461~1 463 89.2 10 1 350 82~90；891~899；434~442；519~527；416~424；886~894；420~428；694~702；891~899；888~896 88.9 10 1 950 13、2 135~2 142；379~387；407~415；281、2 135~2 142；293~301；428~436；
1 058~1 066；450~458；413、1 452~1 459；1 452~1 46088.6
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表 5 同一起始波段不同特征波段的SPA-BP神经网络平均识别率
Table 5. Average recognition rate of SPA-BP neural network with the same starting band and different characteristic bands
起始波
段/nm特征值
数/个平均识
别率/%起始波
段/nm特征值
数/个平均识
别率/%1 445 5 92.3 1 445 10 90.6 1 445 7 93.0 1 445 20 92.7 1 445 9 93.2 1 445 25 91.2 1 445 8 91.6
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表 6 同一预处理方式10种木材的SPA-BP神经网络平均识别率
Table 6. Average recognition rate of SPA-BP neural network for 10 kinds of wood with the same pretreatment method
木材种类 平均识
别率/%木材种类 平均识
别率/%木材种类 平均识
别率/%红檀 90.9 巴里黄檀 94.2 中美洲黄檀 100.0 大果紫檀 100.0 红檀香 100.0 黑檀 100.0 檀香紫檀 90.7 破布木 94.6 平均 95.7 刺猬紫檀 95.1 木犀科豆瓣香 91.0 说明：预处理方式为移动平均法+SNV，起始波段为1 445 nm,
特征值数为9个
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