OPTICS-based Unsupervised Method for Flaking Degree Evaluation on the Murals in Mogao Grottoes.
OPTICS-based Unsupervised Method for Flaking Degree Evaluation on the Murals in Mogao Grottoes.
复制标题
基于光学的莫高窟壁画剥落程度无监督评估方法
DOI：
10.1038/s41598-018-34317-7
复制
发表时间：
2018-10-29
影响因子：
4.6
通讯作者：
Chai B
中科院分区：
文献类型：
--
作者：
Li P;Sun M;Wang Z;Chai B
近年来，莫高窟壁画的预防性保护修复工作受到广泛关注。由于壁画的易碎性和剥离性，有必要研究非接触性疾病检测和预防方法。本文提出了一种非监督的方法来准确预测莫高窟壁画剥落的程度。高光谱图像是用V10-PS高光谱相机拍摄的。该方法主要包括三个步骤：(1)分别用主成分分析(PCA)和稀疏自动编码法(SAE)提取HSI的光谱特征；(2)根据排序点对提取的特征进行聚类，从而识别基于密度的聚类结构(OPTICS)算法；(3)计算聚类核心点与特征空间中其他点的距离，并将最终的分类结果可视化。与现有的高光谱分类工作不同，本文提出的研究内容是壁画剥落程度的检测。由于剥落的程度是连续的，而且工作是在没有任何监督信息的情况下进行的，整个工作流程复杂而具有挑战性。实验结果表明了该方法的有效性。
In recent years, the preventive protection and restoration work of the murals in Mogao Grottoes has received extensive attention. Due to the fragility and detachment of the murals, it is necessary to study non-contact disease detection and prevention methods. In this paper, we propose an unsupervised method to accurately predict the degree of mural flaking diseases in Mogao Grottoes. The hyperspectral image (HSI) is captured by V10-PS hyperspectral camera. The proposed method includes three main steps: (1) extract the spectral features of the HSI by Principal Component Analysis (PCA) and Sparse Auto-Encoder (SAE) respectively; (2) cluster the extracted features by the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm based on the density; (3) calculate the distance between the cluster core point and the other points in the feature space and visualize the final classification result. Different from other existing hyperspectral classification works, the research proposed in this paper is the degree detection of flaking of murals. Since the degree of flaking is continuous and the work is conducted without any supervision information, the entire workflow is complex and challenging. The experimental results show the effectiveness of our method.