In recent years, the preventive protection and restoration work of the murals in Mogao Grottoes has received extensive attention. Due to the fragility and detachment of the murals, it is necessary to study non-contact disease detection and prevention methods. In this paper, we propose an unsupervised method to accurately predict the degree of mural flaking diseases in Mogao Grottoes. The hyperspectral image (HSI) is captured by V10-PS hyperspectral camera. The proposed method includes three main steps: (1) extract the spectral features of the HSI by Principal Component Analysis (PCA) and Sparse Auto-Encoder (SAE) respectively; (2) cluster the extracted features by the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm based on the density; (3) calculate the distance between the cluster core point and the other points in the feature space and visualize the final classification result. Different from other existing hyperspectral classification works, the research proposed in this paper is the degree detection of flaking of murals. Since the degree of flaking is continuous and the work is conducted without any supervision information, the entire workflow is complex and challenging. The experimental results show the effectiveness of our method.
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