Spectral reflectance reconstruction based on multi-kernel support vector regression

Li-juan ZHAO; Hui-qin WANG; Ke WANG; Zhan WANG; Jia-lin LIU; Lei YANG

doi:10.3788/YJYXS20183312.1008

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Spectral reflectance reconstruction based on multi-kernel support vector regression

Image Processing | 更新时间：2020-08-12

- Spectral reflectance reconstruction based on multi-kernel support vector regression
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 12, Pages: 1008-1018(2018)
- 作者机构：
  
  1.西安建筑科技大学信息与控制工程学院, 陕西西安 710055
  2.陕西文物保护研究院, 陕西西安 710075
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20183312.1008
  CLC： O433.4
- Received：05 July 2018，
  
  Accepted：11 September 2018，
  
  Published：2018-12
- 稿件说明：
移动端阅览
Li-juan ZHAO, Hui-qin WANG, Ke WANG, et al. Spectral reflectance reconstruction based on multi-kernel support vector regression[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1008-1018.
DOI：

Li-juan ZHAO, Hui-qin WANG, Ke WANG, et al. Spectral reflectance reconstruction based on multi-kernel support vector regression[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1008-1018. DOI： 10.3788/YJYXS20183312.1008.

摘要

针对传统方法重建光谱反射率过程中未考虑多光谱训练数据维度高、冗余大的特点，导致重建精度低、重建模型学习能力和泛化能力较差的问题，提出了多核支持向量回归的光谱反射率重建方法。首先综合全局、局部核函数的特点引入柯西核函数与多项式核函数的乘积作为多核核函数，然后使用试凑法从训练样本中获取提高模型性能的参数。最后使用多核支持向量回归模型对测试样本进行反射率重建，通过光谱误差及适应度等进行评价。实验结果表明：与伪逆、单核支持向量回归法相比，本文重建方法的光谱误差值降低了0.4~0.785，决策系数提高了2.84~5.27%，平均适应度系数值提高了2%~3%。本文方法在颜色复制中重建精度高、色差较小，满足人眼视觉可容忍范围内。

Abstract

In the process of spectral reflectance reconstruction by traditional methods

it doesn't take into account of the characteristics of highly dimensional and redundant multi-spectral training data

which results in low reconstruction accuracy

poor learning ability and generalization ability of the reconstruction model. In order to solve such problems

the spectral reflectance reconstruction of multi-kernel support vector regression was proposed. Firstly

the product of Cauchy kernel function and polynomial kernel function was introduced as multi-kernel function combining with the characteristics of overall situation and part of kernel function

and then the parameters to improve the performance of the model were obtained from the training samples by cut-and-trial. Finally

the multi-kernel support vector regression model was used to reconstruct the reflectance of the test samples

and evaluation was made through spectral error and fitness. The experimental results show that compared with the pseudo-inverse and single-kernel support vector regression

the spectral error of the reconstruction method is reduced by 0.4-0.785

the decision-making coefficient is increased by 2.84-5.27%

and the average fitness coefficient is increased by 2%-3%. In color reproduction

the reconstruction accuracy is high and the chromatic aberration is small

so that it can meet the tolerance range of human vision.

关键词

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references

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Ke WANG

Hui-qin WANG

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School of Management, Xi'an University of Architecture And Technology

Shaanxi Provincial Institute of Cultural Relics Protection

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