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Estimation of Soluble Salt Concentration in Murals Based on Spectral Transformation and Feature Extraction Modelling
Abstract
Affected by temperature and humidity in the environment, salt crystals expand and accumulate on the surface of murals, causing the pigment layer to peel off, which damages the mural. A method for the rapid and nondestructive detection of salt content in murals using hyperspectral techniques is proposed. The spectral data from mural samples were collected with a spectroradiometer and preprocessed by removing breakpoints with Savitzky‒Golay smoothing. The raw spectra were subjected to continuum removal, logarithm of the reciprocal (LR) processing, multiple scattering correction, and standard normal variate transformation combined with first-order differentiation (FD) and second-order differentiation processing to obtain 15 transformed spectra of different forms. The spectra of samples at different concentration levels were classified, and the characteristic wavelengths were extracted using sample set partitioning based on the joint X–Y distance and successive projection algorithm. The pearson correlation coefficient and variable importance in the projection were used for comparison. Salt concentration estimation models were developed using partial least squares regression (PLSR), support vector regression (SVR), and a random forest (RF) model. The slopes of fit were calculated and compared. The results showed that the reflectance spectra decreased and then increased with increasing salt concentration. The accuracy of RF and SVR was better than that of PLSR, and the Rc2, RMSEc, and RPDc values of the RF-LR-FD model were 0.9703, 0.0466, and 16.8350, respectively. Spectral analysis combined with machine learning models has potential for the nondestructive detection of salt in murals.
About the Authors
Z. Q. Guo
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture; Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring
China
China
Beijing
S. Q. Lyu
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture; Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring
China
China
Beijing
M. L. Hou
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture; Beijing Key Laboratory for Architectural Heritage Fine Reconstruction & Health Monitoring
China
China
Beijing
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Review
For citations:
Guo Z.Q., Lyu S.Q., Hou M.L. Estimation of Soluble Salt Concentration in Murals Based on Spectral Transformation and Feature Extraction Modelling. Zhurnal Prikladnoii Spektroskopii. 2023;90(5):808.
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