摘要
景德镇青花瓷是我国最具代表性的陶瓷品种之一,具有很高的学术价值和社会经济价值。不同年代的青花瓷仅靠外观难以区分,如何快速准确区分不同年代的景德镇青花瓷是文物保护界面临的一个难题。高光谱技术是一种完全无损的分析方法,并已经在壁画、字画等文物的颜料分析中得到成功应用,目前还没有关于瓷器文物的高光谱研究成果出现。选取28个历代景德镇青花瓷碎片样本,使用地面光谱仪测量样本胎釉和青花料部位反射率光谱,分析其典型光谱特征,并对历代青花料光谱特征参量变化趋势进行了分析。研究表明,青花料部位在可见近红外波段光谱特征较为显著,历代青花料光谱特征参量有较明显的差异,并有一定的变化规律。高光谱技术对于景德镇青花瓷的断代研究有很大潜力。
Jingdezhen blue and white porcelain is one of the most representative ceramic types in China,which is famous for its high academic and economic values.However,it is hard to discriminate blue and white porcelains of different time periods from their appearance,and how to solve this problem quickly and accurately is a major challenge to preservation of cultural relics.As a totally nondestructive technique,hyperspectral remote sensing has been successfully applied in pigment analysis of historic frescoes and paintings.In this study,28 Jingdezhen blue and white porcelain samples of different time periods were collected,and their reflectance spectra of both bodies and cobalt blue materials were measured by ground spectrometer.The typical spectral features of blue and white porcelain were summarized,and the change trend of spectral feature parameters for cobalt blue material was analyzed.The study indicated that cobalt blue material has abundant spectral features in visible to near infrared bands,and the spectral feature parameters of different cobalt blue material types showed obvious difference.Hyperspectral remote sensing has significant potential in the cohort analysis of Jingdezhen blue and white porcelains.
作者
赵恒谦
强加成
赵红蕊
赵学胜
ZHAO Heng-qian;QIANG Jia-cheng;ZHAO Hong-rui;ZHAO Xue-sheng(College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing, Beijing 100083, China;Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Beijing 100084, China;3S Center, Tsinghua University, Beijing 100084, China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2019年第3期942-947,共6页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(41701488)资助
关键词
景德镇青花瓷
高光谱技术
光谱特征分析
Jingdezhen blue and white porcelain
Hyperspectral remote sensing
Spectral feature analysis