The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 793–800, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-793-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 793–800, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-793-2022
 
30 May 2022
30 May 2022

HYPERSPECTRAL INVERSION OF SOLUBLE SALT CONTENT IN MURAL PAINTING

Z. Q. Guo1,2, S. Q. Lyu1,2, M. L. Hou1,2, and M. Huang1,2 Z. Q. Guo et al.
  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, No.15 Yongyuan Road, Daxing District, Beijing, China
  • 2Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring, No.15 Yongyuan Road, Daxing District, Beijing, China

Keywords: Mural Salt Damage, Spectral Reflectance, Inversion Model, Linear Regression, Spectral Transformation

Abstract. Mural painting is one of the carriers expressing history and culture. Due to the natural and anthropogenic factors, the salt in mural painting and environment is enriched in the surface layer with temperature change. It will induce irreversible diseases such as crispy alkali, which is not conducive to the survival of mural painting in the present. An efficient and non-destructive method to detect salt in murals is of great importance. Therefore, we proposed a method to predict the soluble salt content of mural paintings based on hyperspectral techniques. First, simulated samples with different salt concentrations were measured by a special spectroradiometer to acquire their spectra. Next, breakpoint correction and average smoothing preprocessing are performed and the data set is divided. Then, the spectra were enhanced by continuum removal (CR) and the logarithm of reciprocal (LR). The salt concentration was correlated with the spectra to extract 10 characteristic bands. Finally, the salt content prediction model was established by simple linear regression (SLR) and multiple linear regression (MLR). The accuracy of the model was evaluated with the coefficient of determination R2, root mean square error RMSE, and relative percent deviation RPD. The experimental results show that the best inversion fit is based on the combination of the CR-MLR model at the strong correlation bands of 420nm, 584nm, and 2379nm (Calibration Set R2 = 0.846, RMSE = 0.138, and RPD = 3.240). This paper provides a new technical means for the non-destructive detection of salt content in murals.