## 3 Results and Discussion
### Neural Network Classification Models' Evaluations
This explains why the red lake endmember mapped consistently to the area of the second application of red lake as it is close to being an optically thick layer of red lake. Looking at the maps from the CNN models and the LS-SAM models, differences in the class labeling performance emerge. First, all four classified regions of the orange-red leaves as \"red lead\" correctly and \"red lake on red lead\" only partially correctly. The misclassifications are in the areas of the second application of red lake that the truth map had labeled as red lake. None of the four assigned it as \"red lake\"; the Derivative CNN model classified it as \"red lake over red lead\", which is correct; interestingly, the other three classified it as either \"red lake over red lead\" or \"red lake over vermilion\", which is incorrect. In summary, examining in detail the small but complex painted areas of the illumination, we observe that the Derivative CNN yielded the most accurate mapping results. Thus, overall, the four methods correctly classified the general regions of pigments in the RIS image cubes, but the Derivative CNN outperformed in the most complex areas of paint application. Given the pigment classification labeled map results of the four classification methods, a test of the Reflectance and Derivative CNN models was performed on a second illumination from the same choir book, _Initial_\(D\) (1964.8.1223) and compared to the material \"truth\" map made using the ENVI-SHW tool. The labeled class results from the Derivative CNN model were similar, if not better, to those from the Reflectance CNN model (see Supplementary Figure S4 for comparison); thus, only the labeled class maps from the Derivative CNN model versus the ENVI-SHW truth map are presented here (Figure 8c). Figure 7: **(a) Details of [PERSON] [PERSON]’s face in test illumination _Initial_\(I\)_with [PERSON]_ (1964.8.1226) and corresponding pigment-labeled class maps from (**b**) ENVI-SHW; (**c**) Reflectance CNN; (**d**) Derivative CNN; (**e**) Reflectance LS-SAM; (**f**) Derivative LS-SAM. **  
A semi-manual analysis with the ENVI-SHW tool (Figure 8b) showed that eleven spectral endmembers were needed to describe the reflectance image cube (see Figure S3); as described earlier, a spectroscopic analysis was performed to label the endmembers with the pigments dominant in the diffuse reflectance spectra. The pigment-labeled classification \"truth\" map shows that a similarly colorful palette to that found in _Initial I with David_ was selected to create _Initial D_. Azurite, with varying amounts of black and white, was used to paint the blue dragons and the central blue flower, with additional touches of black and white to emphasize bodily features such as curves and eyes. Consistent with _Initial I with David_, the green leaf decoration was painted with malachite, highlighted with yellow outlines of a lead-tin yellow overlay, while the bulk of the orange-red flowers were painted with red lead and shaded by an insect-based red lake paint. Three bands of white-, yellow-, and light brown-colored paint outline the interior edges of the dragons' bodies, which define the initial D shape; the latter two colors are made of lead-tin yellow and yellow-brown ochre, respectively. Similar to _Initial I with [PERSON]_, only one endmember was kept for the gold applications in this initial in the region to the right of the illumination, enveloped in the blue bodies of the dragons. No endmembers were found for the background gold foil and parchment and, thus, were not mapped. One endmember was recovered to classify the two purple central flowers within the initial, and an examination of the endmember reflectance spectra revealed absorption features at \(\sim\)526 and 569 nm and a concave rising reflectance beyond 600 nm with a weak broad band absorption around 650 nm.