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1.西安建筑科技大学 信息与控制工程学院,陕西 西安 710055
2.陕西省文物保护研究院,陕西 西安 710075
Received:09 February 2021,
Revised:08 March 2021,
Published:15 October 2021
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曹赤鹏,王慧琴,王可等.光谱色差与主分量特征融合的石窟表面风化智能评估[J].光学精密工程,2021,29(10):2444-2455.
CAO Chi-peng,WANG Hui-qin,WANG Ke,et al.Intelligent evaluation of grotto surface weathering based on spectral chromatic aberration and principal component feature fusion[J].Optics and Precision Engineering,2021,29(10):2444-2455.
曹赤鹏,王慧琴,王可等.光谱色差与主分量特征融合的石窟表面风化智能评估[J].光学精密工程,2021,29(10):2444-2455. DOI: 10.37188/OPE.20212910.2444.
CAO Chi-peng,WANG Hui-qin,WANG Ke,et al.Intelligent evaluation of grotto surface weathering based on spectral chromatic aberration and principal component feature fusion[J].Optics and Precision Engineering,2021,29(10):2444-2455. DOI: 10.37188/OPE.20212910.2444.
针对单一光谱特征无法精细化表征石窟表面复杂风化区域风化类型及程度的问题,基于光谱分析与色度学理论,提出了一种光谱色差与主分量特征融合的石窟表面风化智能量化评估方法。重建石窟表面多光谱图像的反射光谱,计算每个像素点与基准点的色差,并使用主成分分析方法提取多光谱图像数据的主分量特征;然后融合石窟表面风化的光谱色差与主分量特征,对不同风化类型及程度进行表征;最后使用随机森林分类器,对石窟表面多光谱图像中每个像素点的风化程度进行智能评估。实验表明,融合光谱色差与主分量特征的方法对复杂风化区域不同风化类型及程度的表征能力优于单一光谱特征,石窟表面整体风化程度评估准确率为99.86%,Kappa系数为0.99,可实现复杂风化区域的精细化表征。
To overcome the problem of a single spectral feature not finely characterizing the type and degree of weathering in the complex weathering area on a grotto surface
this paper proposes an intelligent quantitative evaluation method for grotto surface weathering based on spectral analysis and colorimetric theory. First
we reconstruct the reflection spectrum of the multispectral image of the grotto surface
calculate the color difference between each pixel and reference point
and use the principal component analysis method to extract the principal component features from the multispectral image data. Then
we fuse the spectral color difference and principal component features of the weathering on the grotto surface to characterize different types and degrees of weathering. Finally
a random forest classifier is used to intelligently evaluate the weathering degree of each pixel in the grotto surface multispectral image. Experiments show that the method of fusing spectral chromatic aberration and principal component features performs better compared with a single spectral feature in characterizing different types and degrees of weathering in complex weathering areas. The evaluation accuracy of the overall grotto surface weathering degree is 99.86%
with a Kappa coefficient of 0.99. The proposed method can effectively realize a refined characterization of complex weathered areas.
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