Unsupervised hyperspectral image segmentation of films: a hierarchical clustering-based approach

Hyperspectral imaging (HSI) has been drastically applied in recent years to cultural heritage (CH) analysis, conservation, and also digital restoration. However, the efficient processing of the large datasets registered remains challenging and still in development. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative machine learning approach to the most common practices, such as principal component analysis(PCA). HCA has shown its potential in the past decades for spectral data classification and segmentation in many other fields, maximizing the information to be extracted from the high-dimensional spectral dataset via the formation of the agglomerative hierarchical tree. However, to date, there has been very limited implementation of HCA in the field of cultural heritage. Data used in this experiment were acquired on real historic film samples with various degradation degrees, using a custom-made push-broom VNIR hyperspectral camera (380–780nm). With the proposed HCA workflow, multiple samples in the entire dataset were processed simultaneously and the degradation areas with distinctive characteristics were successfully segmented into clusters with various hierarchies. A range of algorithmic parameters was tested, including the grid sizes, metrics, and agglomeration methods, and the best combinations were proposed at the end. This novel application of the semi-automating and unsupervised HCA could provide a basis for future digital unfading, and show the potential to solve other CH problems such as pigment mapping.