首页|一种混合颜料光谱分区间识别方法

一种混合颜料光谱分区间识别方法

扫码查看
古代彩绘颜料的分析是科技考古与文物保护研究的重要内容,对探索古代颜料技术发展和科学保护文物有着重要的学术价值和现实意义,传统的颜料识别算法大多针对彩绘文物表面的纯净颜料,对文物表面存在混合颜料的识别准确度较差.化学分析需要对壁画表面进行取样,易造成损伤.高光谱是近年来发展迅速的新兴技术,在物质识别上具有广泛应用,提出一种基于高光谱分区间的混合颜料识别方法.首先,将未知颜料反射光谱进行一阶导数计算,根据未知颜料一阶导数曲线的"凸起"确定特征子区间范围,若子区间数量大于2,则只保留"凸起"最明显的两个特征子区间;其次,通过KM模型将反射率转换为更符合线性混合特征的吸收散射比(K/S),在特征子区间范围内,对未知颜料与标准颜料的K/S曲线进行最大最小值归一化,运用光谱角余弦结合归一化欧氏距离的方法计算未知颜料与标准颜料K/S曲线的相似度,每个特征子区间保留相似度最高的前三个结果;最后,在每个特征子区间的识别结果中依次取出标准颜料K/S曲线,不同特征子区间所取出的标准颜料K/S曲线两两组合成子区间识别结果集合,再根据狄利克雷分布函数得到的丰度矩阵,生成1 000条不同丰度的模拟混合K/S曲线,将模拟混合K/S曲线与未知颜料K/S曲线再次进行相似度计算,每个区间识别结果均保留一个最高值,所有集合保留值中再次取最高,该值对应的区间识别结果集合中的标准颜料即为未知颜料的最终识别结果.实验选取石青、石绿、石黄、朱砂颜料制作纯净颜料样本,并将四种纯净颜料两两混合制作六组混合颜料样本,样本绘制完成后使用高光谱成像仪采集样本的成像数据,通过预处理后使用上述方法进行特征子区间的提取与识别.实验发现,仅石绿和朱砂混合样本中的朱砂出现识别错误,该方法对混合颜料样本识别结果良好,整体识别率为83.3%.结果表明该方法可以对混合颜料进行识别,对文物颜料分析具有实践意义.
A Mixed Pigment Identification Method Based on Spectra Interval
The analysis of ancient painted pigments is an important content of technological archaeology and cultural relics conservation research,which has important academic value and practical significance for exploring the development of ancient pigment technology and scientific conservation of cultural murals.Most of the traditional pigment identification algorithms are aimed at the pure pigments on the surface of painted cultural murals,whose identification accuracy is relatively poor for the mixed pigments on the surface of cultural relics.At the same time,chemical analysis methods usually require sampling of the surface of murals,which can easily cause damage.Hyperspectral technology is an emerging technology that has developed rapidly in recent years and has wide application in material identification.A method of mixed pigment identification based on hyperspectral intervals is proposed.Firstly,the first-order derivative of the reflectance spectrum of the unknown pigment is calculated,and the characteristic subinterval range is determined according to the"bump"of the first-order derivative curve of the unknown pigment.If the number of subintervals exceeds 2,only the two most obvious"raised"subintervals will be retained.Secondly,the reflectance curves of the unknown pigment and the standard pigment are transformed to the absorption-scattering ratio(K/S)using the KM model,which aligns more with the linear mixing characteristics.These K/S curves are normalized to[0,1]within the characteristic subinterval range.We calculate the similarity between the K/S curves of the unknown pigment and the standard pigments using the Spectral Angle Cosine combined with the Normalized Euclidean Distance.The top three results with the highest similarity for each characteristic subinterval are selected.Finally,one standard pigment K/S curve is removed from the identification results for each characteristic subinterval.Different standard pigment K/S curves from different characteristic subintervals are combined individually to generate the collection of subinterval identification results.They are combined with the abundance matrix obtained from the Dirichlet distribution function to generate 1 000 simulated mixed K/S curves.The similarity between the simulated mixed K/S spectra and the unknown pigment K/S curves is calculated again.We select one standard pigment with the highest similarity value in each collection.The similarity is compared again,and only the pigment with the highest value is identified as the final pigment for the unknown pigment spectra.The pure pigment samples were made by selecting Azurite,Malachite,Orpiment,and Cinnabar pigments.Six sets of mixed pigment samples were made by mixing pure pigments one by one.After the samples were drawn,the imaging data was collected by a hyperspectral imager.The feature subintervals are extracted,and the pigments are recognized by the proposed method after pre-processing.All the results were identified correctly except the Cinnabar in the mixed samples of Malachite and Cinnabar.The overall recognition rate for the mixed pigment samples is 83.3%.The results show that this method can identify mixed pigments and has practical significance for analyzing cultural relics pigments.

Pigment recognitionFeature extractionSpectra recognitionMixed pigmentsHyperspectral

孙宇桐、吕书强、侯妙乐

展开 >

北京建筑大学测绘与城市空间信息学院,北京 102616

建筑遗产精细重构与健康监测北京市重点实验室,北京 102616

颜料识别 特征提取 光谱识别 混合颜料 高光谱

国家重点研发计划项目国家自然科学基金项目国家自然科学基金项目

2022YFF09044004217135642171444

2024

光谱学与光谱分析
中国光学学会

光谱学与光谱分析

CSTPCD北大核心
影响因子:0.897
ISSN:1000-0593
年,卷(期):2024.44(8)
  • 18