Hyperspectral analysis of cultural heritage artifacts: pigment material diversity in the Gough Map of Britain

Abstract

The Gough Map, one of the earliest surviving maps of Britain, was created and extensively revised over the 13th and 14th centuries. In 2015, map was imaged using a hyperspectral imaging system while in the collection at the Bodleian Library, Oxford University. The goal of the collection of the hyperspectral image (HSI) of the Gough Map was to address questions such as enhancement of faded text for reading and analysis of the pigments used during its creation and revision. In particular, pigment analysis of the Gough Map may help historians understand the material diversity of its composition and potentially the timeline of, and methods used in, the creation and revision of the map. Multiple analysis methods are presented to analyze a particular pigment in the Gough Map with an emphasis on understanding the within-material diversity, i.e., the number and spatial layout of distinct red pigments. One approach to understanding the number of distinct materials in a scene (i.e., endmember selection and dimensionality estimation) is the Gram Matrix approach. Here, this method is used to study the within-material differences of pigments in the map with common visual color. The application is a pigment analysis tool that extracts visually common pixels (here the red pigments) from the Gough Map and estimates the within material diversity of the pixels. Results show that the Gough Map is composed of at least 5 kinds of dominant red pigments with a particular spatial pattern. This research provides a useful tool for historical geographers and cartographic historians to analyze the material diversity of hyperspectral images of cultural heritage artifacts.

Quantitative analysis of mixed pigments for Chinese paintings using the improved method of ratio spectra derivative spectrophotometry based on mode

“…Before extracting endmembers, it is sometimes necessary to perform component number estimation. The currently used methods include orthogonal projection, spectral clustering, geometrical and statistical approaches [7][8][9][10][11][12][13][14], which are selected to estimate the "true" or "pure" spectral components contained on the image, and some prior knowledge is usually required in some steps to eliminate possibly unimportant endmembers [8,13,14]. On the other hand, each pigment has its specific reflective spectrum.…”

Deep learning for the extraction of sketches from spectral images of historical paintings

“…Compared with ordinary images, spectral images not only contain spectral information but also combine spectral with spatial information. In recent years, the rapid development of spectral imaging in terrestrial applications has also influenced the field of cultural heritage restoration [32][33][34][35][36][37] .…”

Review of Methods for Documentation, Management, and Sustainability of Cultural Heritage. Case Study: Museum of King Jan III’s Palace at Wilanów

“…On the other hand, this knowledge allows the art historians for verification of the object's authenticity. Pigment identification commonly involves the use of VIS-NIR range of spectrum [117,120,124,126,147,148] as the signatures of most inorganic pigments vary significantly in this range, so the spectral information is sufficient to differentiate them. However, to tell apart organic materials like binders, varnishes, or organic pigments, the spectrum of longer wavelengths like SWIR and MWIR is usually needed [149].…”