Laser & Optoelectronics Progress, Volume. 57, Issue 1, 013003(2020)

Multispectral Dimension Reduction Algorithm Based on Partial Least Squares

Qiulan Yang, Xiaoxia Wan*, and Gensheng Xiao
Author Affiliations
  • Department of Printing and Packaging, Wuhan University, Wuhan, Hubei 430072, China
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    Figures & Tables(5)
    Chromaticity diagrams of 1600 Munsell color blocks obtained by CIE D65 and CIE 1931 standard observers. (a) L*-a* chromaticity diagram; (b) L*-b* chromaticity diagram; (c) a*-b* chromaticity diagram
    Spectral error distributions obtained by different dimension reduction methods. (a) Dimension reduction by LabPQR method; (b) dimension reduction by LabKMN method
    Spectral reconstruction RMSE curves obtained by different dimension reduction methods. (a) Dimension reduction by LabPQR method; (b) dimension reduction by LabKMN method
    Fitted curves of reconstructed spectra for different samples. (a) Sample with good fitted effect; (b) sample with general fitted effect; (c) sample with poor fitted effect
    • Table 1. Comparison of spectral reconstruction accuracy between LabPQR dimension reduction method and LabKMN dimension reduction method

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      Table 1. Comparison of spectral reconstruction accuracy between LabPQR dimension reduction method and LabKMN dimension reduction method

      Dimensionreduction methodRMSEΔEab*(D65)ΔEab* (A)Sampleeligibility rate /%
      MaxMinMeanMaxMinMeanMaxMinMean
      LabPQR0.11780.00240.01648.39300.18652.921410.59460.19492.819784.37
      LabKMN0.07700.00220.01395.72200.09801.502511.74580.03832.125195.28
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    Qiulan Yang, Xiaoxia Wan, Gensheng Xiao. Multispectral Dimension Reduction Algorithm Based on Partial Least Squares[J]. Laser & Optoelectronics Progress, 2020, 57(1): 013003

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    Paper Information

    Category: Spectroscopy

    Received: Jun. 4, 2019

    Accepted: Jul. 22, 2019

    Published Online: Jan. 3, 2020

    The Author Email: Wan Xiaoxia (wan@whu.edu.com)

    DOI:10.3788/LOP57.013003

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