Hyperspectral Pigment Analysis of Cultural Heritage Artifacts Using the Opaque Form of Kubelka-Munk Theory

摘要

Kubelka-Munk (K-M) theory has been successfully used to estimate pigment concentrations in the pigmentmixtures of modern paintings in spectral imagery. In this study the single-constant K-M theory has been utilizedfor the classi cation of green pigments in the Selden Map of China, a navigational map of the South China Sealikely created in the early seventeenth century. Hyperspectral data of the map was collected at the BodleianLibrary, University of Oxford, and can be used to estimate the pigment diversity, and spatial distribution, withinthe map. This work seeks to assess the utility of analyzing the data in the K/S space from Kubelka-Munktheory, as opposed to the traditional reectance domain. We estimate the dimensionality of the data and extractendmembers in the reectance domain. Then we perform linear unmixing to estimate abundances in the K/Sspace, and following Bai, et al. (2017), we perform a classi cation in the abundance space. Finally, due to thelack of ground truth labels, the classification accuracy was estimated by computing the mean spectrum of eachclass as the representative signature of that class, and calculating the root mean squared error with all the pixelsin that class to create a spatial representation of the error. This highlights both the magnitude of, and anyspatial pattern in, the errors, indicating if a particular pigment is not well modeled in this approach.