A Novel Hyperspectral Salt Assessment Model for Weathering in Architectural Ruins

Abstract. The Dunhuang murals, a significant part of Chinese heritage, have suffered deterioration primarily due to environmental and chemical factors, notably salt damage. This study proposes a sophisticated method that synergizes Fractional Order Differentiation (FOD) and Partial Least Squares Regression (PLSR) to accurately invert the phosphate content in the Mural Plaster of the Dunhuang paintings. The focal points of the research include: 1) To address the issue of information loss and reduced modeling precision caused by integer order differentiation algorithms, the FOD method is employed for preprocessing hyperspectral data. This approach ensures the fine spectral differences in the phosphate content of the Mural Plaster are precisely captured, 2) Utilizing PLSR, the study models the spectral bands identified at a significance level of 0.01 with measured conductivity values, thereby enabling the precise prediction of the phosphate content in the murals. The research outcomes reveal: 1) The FOD method can elucidate the nonlinear characteristics and variation patterns of the mural samples in the hyperspectral curve.As the order increases from zero to two, the number of spectral bands meeting the 0.01 significance test initially decreases and then increases. The highest absolute value of the positive correlation coefficient is observed at 1.9 orders, corresponding to the 2077 nm band, 2) For predicting the phosphate content in the murals, the model at 1.9 orders is most suitable for inversion. This model, after cross-validation, achieves a maximum R2 value of 0.783. This study created an efficient FOD-based model for estimating phosphate in mural plasters.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: A Novel Hyperspectral Salt Assessment Model for Weathering in Architectural Ruins ; volume:X-2-2024 ; year:2024 ; pages:201-208 ; extent:8 ; ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; X-2-2024 (2024), 201-208 (gesamt 8)

Classification and Topics

• Classification: Geowissenschaften

Contributors, Places and Time

• Creator: Ren, Yikang ; Liu, Fang

Further information

• DOI: 10.5194/isprs-annals-X-2-2024-201-2024
• URN: urn:nbn:de:101:1-2408051421516.921836973851
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:44 AM CEST