What's wrong with the murals at the Mogao Grottoes : a near-infrared hyperspectral imaging method
Sun, Meijun and Zhang, Dong and Wang, Zheng and Ren, Jinchang and Chai, Bolong and Sun, Jizhou (2015) What's wrong with the murals at the Mogao Grottoes : a near-infrared hyperspectral imaging method. Scientific Reports, 5. 14371. ISSN 2045-2322 (https://doi.org/10.1038/srep14371)
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Abstract
Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise.
ORCID iDs
Sun, Meijun, Zhang, Dong, Wang, Zheng, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194, Chai, Bolong and Sun, Jizhou;-
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Item type: Article ID code: 55672 Dates: DateEvent23 September 2015Published17 August 2015AcceptedSubjects: Technology > Engineering (General). Civil engineering (General) Department: University of Strathclyde > University of Strathclyde
Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 24 Feb 2016 10:01 Last modified: 11 Nov 2024 11:10 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/55672