摘要
颜料层脱落区域的提取是壁画现状调查的重要环节,由于其光谱特征与壁画白色图案较为相似,仅利用光谱特征提取的精度较低。因此,提出了一种兼顾光谱特征和Hu矩形状特征的颜料层脱落区域提取方法。首先,利用壁画高光谱图像的光谱信息,经光谱降维,采用支持向量机监督分类法提取颜料层脱落区域与白色图案。然后,对颜料层脱落区域与白色图案分类结果分别进行连通,将连通后的图斑视为最小识别对象,利用Hu矩计算每一个对象的形状特征,采用支持向量机二分类再次区分对象图斑,实现颜料层脱落区域的半自动提取。最后,以青海省瞿昙寺壁画高光谱图像进行了提取。结果表明,该方法能提高颜料层脱落区域的提取精度,为壁画的现状调查提供支撑。
The extraction of mural paint loss plays an important role in investigating the present situation of murals.Given the similarity of spectral features of paint loss to the white patterns of the mural,the only utilization of spectral features would make it less accurate for the extraction of the mural paint loss.Aiming at improving the extraction performance,a comprehensive method was proposed that integrated spectral features and Hu moment.First,the supervised support vector machine method was employed to classify paint loss and white patterns by the spectral features which the dimension had been reduced.The classified pixels of paint loss and white patterns were then connected to form two types of polygons,which were regarded as the smallest indivisible objects.Subsequently,seven Hu moments for each polygon were calculated as the shape features serving as the feature vectors to distinguish each polygon again based on the support vector machine method.In this way,the semi-automatic extraction of mural paint loss was realized.A case study was conducted to evaluate the performance of our proposed method,using the hyperspectral data of the Qutan Temple mural.The results show that our proposed method is capable of enhancing the extraction accuracy of mural paint loss and supporting the investigation of the present situation of murals.
作者
曹鹏辉
吕书强
汪万福
高振华
侯妙乐
CAO Peng-hui;LYU Shu-qiang;WANG Wan-fu;GAO Zhen-hua;HOU Miao-le(School of Geomatics and Urban Spatial Informatics,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Beijing Key Laboratory for Architectural Heritage Fine Reconstruction&Health Monitoring,Beijing 100044,China;Dunhuang Academy China,Dunhuang Gansu 736200,China;Shanxi Provincial Institute of Archaeology,Taiyuan Shanxi 030000,China)
出处
《图学学报》
CSCD
北大核心
2020年第6期930-938,共9页
Journal of Graphics
基金
国家自然科学基金项目(41371492)
国家重点研发计划项目(2017YFB1402105)
北京市属高校高水平教师队伍建设支持计划长城学者培养计划项目(CIT&TCD20180322)
北京建筑大学研究生创新项目(PG2020070)。
关键词
颜料层脱落
病害提取
壁画
高光谱成像
HU矩
支持向量机
最小噪声分离
paint loss
disease extraction
mural
hyperspectral imaging
Hu moment
support vector machine
minimum noise fraction
