8 May 2018 Pigment diversity estimation for hyperspectral images of the Selden map of China
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Abstract
The Selden Map of China, an early 17th century wall map of East Asia was rediscovered in 2008. Recently, the map was imaged using a VNIR 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 Selden Map was to help historians understand the material diversity of its composition and potentially the methods used in the creation of the map. The Selden map has been named one of the ”Treasures of the Bodleian” and it poses many questions such as the diversity of pigments used to create the map. In this research, we extract visually common pixels (here, the green pigments) from the Selden Map and estimate the material diversity of the green pixels. Previous pigment analysis on the HSI of the Gough Map1, 2 used an endmember based approach, the Gram Matrix technique,3, 4 to understand the number of distinct materials in a scene and then used spectral angle mapper (SAM) to classify all the pigments. Here, we use the same Gram Matrix technique, but due to the complexity of the Selden Map data, instead of using SAM, we use two spectral unmixing techniques, NNLS (nonnegative linear least squares)5 and FUMI (functions of multiple instances)6 to determine the weights of all the endmembers for each data point to study the within-material diversity. Results show that the Selden Map is composed of at least 6 kinds of dominant green pigments with a particular spatial pattern. This research provides a useful tool for historical geographers and cartographic historians to analyze the material diversity of HSI of cultural heritage artifacts.
Conference Presentation
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Di Bai, Di Bai, David W. Messinger, David W. Messinger, David Howell, David Howell, } "Pigment diversity estimation for hyperspectral images of the Selden map of China", Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064415 (8 May 2018); doi: 10.1117/12.2304041; https://doi.org/10.1117/12.2304041
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