HYPERSPECTRAL INVERSION OF SOLUBLE SALT CONTENT IN MURAL PAINTING
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-05-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/793/2022/isprs-archives-XLIII-B2-2022-793-2022.pdf |
| Summary: | 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 <i>R</i><sup>2</sup>, 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 <i>R</i><sup>2</sup> = 0.846, <i>RMSE</i> = 0.138, and <i>RPD</i> = 3.240). This paper provides a new technical means for the non-destructive detection of salt content in murals. |
|---|---|
| ISSN: | 1682-1750 2194-9034 |