基于样本优化选取的光谱重建方法研究

doi:10.7517/j.issn.1674-0475.2017.01.088

影像科学与光化学 ›› 2017, Vol. 35 ›› Issue (1): 88-96.DOI: 10.7517/j.issn.1674-0475.2017.01.088

• 应用与发展 • 上一篇

基于样本优化选取的光谱重建方法研究

龙艳群¹, 王慧琴^1,2, 王可^1,2, 王展³, 赵素文¹

1. 西安建筑科技大学信息与控制工程学院, 陕西西安 710055;
2. 西安建筑科技大学管理学院, 陕西西安 710055;
3. 陕西文物保护研究院, 陕西西安 710075

收稿日期:2016-07-07 出版日期:2017-01-15 发布日期:2017-01-15
通讯作者: 王慧琴
基金资助:
教育部归国留学人员科研扶持项目（K05055）、教育部高等学校博士学科点专项科研基金（20126120110008）和青年科技基金项目（QN1628）

Study on Spectral Reconstruction Method Based on Optimized Selected Samples

LONG Yanqun¹, WANG Huiqin^1,2, WANG Ke^1,2, WANG Zhan³, ZHAO Suwen¹

1. School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, P. R. China;
2. School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, P. R. China;
3. Shanxi Provincial Institute of Cultural Relics Protection, Xi'an 710075, Shaanxi, P. R. China

Received:2016-07-07 Online:2017-01-15 Published:2017-01-15

摘要/Abstract

摘要：

针对光谱反射率重建中已有样本选取方法的不足，提出了一种基于核模糊C聚类的样本优化选取方法。该方法综合考虑了光谱反射率空间的广泛性和色度空间的相似性，较大程度满足了光谱重建的精度。首先采用已有样本选取法在光谱反射率空间选取C个样本作为聚类初始点，再将原光谱转化到色度空间进行聚类，同时引入核函数将二维色度空间映射到三维特征空间，使得特征数据线性可分，从而达到更好的划分效果。实验结果表明，使用该方法选取训练样本进行光谱反射率重建能够进一步提高光谱重建精度，色度评价和光谱评价结果均好于已有方法。

关键词: 光谱学, 核模糊C聚类, 样本选取, 光谱重建

Abstract:

Aiming at the defect of existing sample selection methods for spectral reflectance reconstruction, a new method based on the kernel fuzzy C-means clustering optimization sample selection is presented.This method considers the extensiveness of spectral reflectance space and the similarity of chroma space, meeting the accuracy of spectral reconstruction. Firstly, C samples are selected as the initial clustered core in the spectral reflectance space. And the original spectrais turned into(a^*,b^*) color space. At the same time, 2D chrominance space is mapped to the 3D feature space with kernel function, making characteristics linearly separable,to achieve a better result.The experiments show that this method selects training samples for spectral reflectance reconstruction can further improve the accuracy of spectral reconstruction, and it is better than the existing methods in the chroma and accuracy assessment effects.

Key words: spectroscopy, kernel fuzzy clustering, sample selection, spectral reconstruction

龙艳群, 王慧琴, 王可, 王展, 赵素文. 基于样本优化选取的光谱重建方法研究[J]. 影像科学与光化学, 2017, 35(1): 88-96.

LONG Yanqun, WANG Huiqin, WANG Ke, WANG Zhan, ZHAO Suwen. Study on Spectral Reconstruction Method Based on Optimized Selected Samples[J]. Imaging Science and Photochemistry, 2017, 35(1): 88-96.

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基于样本优化选取的光谱重建方法研究

Study on Spectral Reconstruction Method Based on Optimized Selected Samples

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