Cluster Analysis of IR Thermography Data for Differentiating Glass Types in Historical Leaded-Glass Windows

Abstract

Infrared thermography is a fast, non-destructive and contactless testing technique which is increasingly used in heritage science. The aim of this study was to assess the ability of infrared thermography, in combination with a data clustering approach, to differentiate between the different types of historical glass that were included in a colorless leaded-glass windows during previous restoration interventions. Inspection of the thermograms and the application of two data mining techniques on the thermal data, i.e., k-means clustering and hierarchical clustering, allowed identifying different groups of window panes that show a different thermal behavior. Both clustering approaches arrive at similar groupings of the glass with a clear separation of three types. However, the lead cames that hold the glass panes appear to have a substantial impact on the thermal behavior of the surrounding glass, thus preventing classification of the smallest glass panes. For the larger panes, this was not a critical issue as the center of the glass remained unaffected. Subtle visual color differences between panes, implying a variation in coloring metal ions, was not always distinguished by IRT. Nevertheless, data clustering assisted infrared thermography shows potential as an efficient and swift method for documenting the material intervention history of leaded-glass windows during or in preparation of conservation treatments.