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Kubelka-Munk (K-M) theory has been successfully used to estimate pigmentconcentrations in the pigment mixtures of modern paintings in spectral imagery.In this study the single-constant K-M theory has been utilized for theclassification of green pigments in the Selden Map of China, a navigational mapof the South China Sea likely created in the early seventeenth century.Hyperspectral data of the map was collected at the Bodleian Library, Universityof Oxford, and can be used to estimate the pigment diversity, and spatialdistribution, within the map. This work seeks to assess the utility ofanalyzing the data in the K/S space from Kubelka-Munk theory, as opposed to thetraditional reflectance domain. We estimate the dimensionality of the data andextract endmembers in the reflectance domain. Then we perform linear unmixingto estimate abundances in the K/S space, and following Bai, et al. (2017), weperform a classification in the abundance space. Finally, due to the lack ofground truth labels, the classification accuracy was estimated by computing themean spectrum of each class as the representative signature of that class, andcalculating the root mean squared error with all the pixels in that class tocreate a spatial representation of the error. This highlights both themagnitude of, and any spatial pattern in, the errors, indicating if aparticular pigment is not well modeled in this approach.

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