Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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. ISSN 2045-2322

[img]
Preview
Text (Sun-etal-SR2015-whats-wrong-with-the-murals-at-the-mogao-grottoes)
Sun_etal_SR2015_whats_wrong_with_the_murals_at_the_mogao_grottoes.pdf - Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (1MB) | Preview

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.