In this work, we present VisuaLayered, the implementation of a combined analysis workflow for pigment identification. VisuaLayered is an integrated, interactive system that focuses on the combined visual analysis of Macro X-Ray Fluorescence (MA-XRF) and Reflectance Imaging Spectroscopy (RIS) data.

Analysing paintings for pigment identification is relevant for many applications in the cultural heritage domain, such as conservation and restoration.
Domain experts use non-invasive scanning techniques as an initial step in their analysis. Two such techniques are MA-XRF and RIS. They provide hyperspectral data on the elemental and molecular composition of pigments, respectively. Domain experts analyse these two complementary data modalities in order to determine the pigments present in the different paint layers of a painting. However, due to the size and high-dimensionality of these datasets, experts have problems with efficiently analysing the data. In general, they examine the two data modalities separately in the analysis workflow and use their knowledge to unify all the information without additional software support, as there is no integrated system that is designed specifically for the combined analysis of MA-XRF and RIS data.

We worked in collaboration with domain experts from the Rijksmuseum, Amsterdam in order to design and implement VisuaLayered based on current domain practices. We use t-SNE for projecting the high-dimensional data into a two dimensional space, which the user can interactively explore in combination with other linked views in order to find connections between the two data modalities. With our system, experts can explore the spatial distribution and the correlation between pixels that have similar molecular and/or elemental compositions. Additionally, for the RIS data, we support endmember identification and analysis based on the pigments' spectral profiles.
We tested the efficiency of our system with respect to the designed workflow in a case study evaluation with our collaborator. They successfully used VisuaLayered for the analysis of one painting and found the views combining the two data modalities very useful for better understanding the relation between them. Moreover, they were able to identify new pigments, that they missed when using existing software.