Multispectral imaging is a preliminary screening technique for the study of paintings. Although it permits the identification of several mineral pigments by their spectral behavior, it is considered less performing concerning hyperspectral imaging, since a limited number of wavelengths are selected. In this work, we propose an optimized method to map the distribution of the mineral pigments used by Vincenzo Pasqualoni for his wall painting placed at the Basilica of S. Nicola in Carcere in Rome, combining UV/VIS/NIR reflectance spectroscopy and multispectral imaging. The first method (UV/VIS/NIR reflectance spectroscopy) allowed us to characterize pigment layers with a high spectral resolution; the second method (UV/VIS/NIR multispectral imaging) permitted the evaluation of the pigment distribution by utilizing a restricted number of wavelengths. Combining the results obtained from both devices was possible to obtain a distribution map of a pictorial layer with a high accuracy level of pigment recognition. The method involved the joint use of point-by-point hyperspectral spectroscopy and Principal Component Analysis (PCA) to identify the pigments in the color palette and evaluate the possibility to discriminate all the pigments recognized, using a minor number of wavelengths acquired through the multispectral imaging system. Finally, the distribution and the spectral difference of the different pigments recognized in the multispectral images, (in this case: red ochre, yellow ochre, orpiment, cobalt blue-based pigments, ultramarine and chrome green) were shown through PCA false-color images.

中文翻译：

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通过多光谱成像结合高光谱光谱法绘制无机颜料的优化方法，用于研究罗马卡塞雷圣尼古拉大教堂 Vincenzo Pasqualoni 的壁画

多光谱成像是绘画研究的初步筛选技术。虽然它允许通过光谱行为识别几种矿物颜料，但在高光谱成像方面被认为性能较差，因为选择的波长数量有限。在这项工作中，我们提出了一种优化方法，结合 UV/VIS/NIR 反射光谱和多光谱成像，绘制 Vincenzo Pasqualoni 在他的壁画中使用的矿物颜料的分布，这些颜料放置在罗马 Carcere 的 S. Nicola 大教堂。第一种方法（UV/VIS/NIR 反射光谱）使我们能够以高光谱分辨率表征颜料层；第二种方法（UV/VIS/NIR 多光谱成像）允许通过使用有限数量的波长来评估色素分布。结合从两种设备获得的结果，可以获得具有高精度颜料识别水平的图像层的分布图。该方法包括联合使用逐点高光谱光谱和主成分分析 (PCA) 来识别调色板中的颜料，并使用通过多光谱获得的少量波长来评估区分所有识别颜料的可能性。成像系统。最后，通过PCA假色图像显示多光谱图像中识别的不同颜料的分布和光谱差异（在这种情况下：红赭石、黄赭石、雌黄、钴蓝基颜料、群青和铬绿） .