12/14/2023 0 Comments Spectra paint![]() ![]() minerals) that make up each class.Ī typical workflow in remote sensing for automatically labeling classes into materials requires a priori knowledge of the area imaged, specifically, sufficient knowledge of what materials are to be expected must be known so that the appropriate spectral library of pure materials can be selected. A material map goes further and identifies the specific materials (e.g. A classification map groups related spectra that comprise a given class, but does not identify the specific materials present. Classification maps help segment large reflectance image cubes into a discrete set of representative spectra (known as endmembers or classes). Generally in remote sensing, the exploitation of reflectance image cubes to make classification and/or material maps has been an active area of research for decades, utilizing both physics-based and data-driven algorithms. Analysis of RIS data cubes of paintings is more challenging, and has typically utilized workflows and algorithms developed for remote sensing of minerals and vegetation. While XRF data can be processed readily to make elemental maps, the direct translation of these into labeled pigment maps is, in general, not possible, as the same element can often be found in more than one pigment (though exceptions occur, such as the element mercury which can usually be assigned to the pigment vermilion in a painted object). The processing of these data cubes has focused on grouping spatial pixels having similar spectral information, allowing visualization of locations on a painted surface that may share a chemical makeup. This produces a spectrum at each spatial pixel in the image cube. ![]() Both modalities consist of numerous narrow spectral band images, thus creating a 3-D image cube, where the first two dimensions are spatial, and the third dimension is spectral. These two modalities provide complementary information that can be used to identify and map many of the pigments over a painting’s surface. The availability of pigment maps for a work of art, where each class is labeled as a specific pigment or pigment mixture, greatly enhances the ability for conservators to analyze paintings.Ĭurrently the most widely used macroscale imaging modalities for art examination are imaging X-ray fluorescence (XRF) spectroscopy, and reflectance hyperspectral imaging (typically 400 to \(\sim 1000\) nanometer (nm) and sometimes out to 2500 nm), otherwise known as reflectance imaging spectroscopy (RIS). Importantly, it also informs conservators and museums on how to better preserve these works based on their materiality, propensity for degradation, or even by identifying degradation products of processes already occurring. This allows for a more robust understanding of an artist’s creative process, and helps answer certain art historical research questions. The development of spectral macroscale mapping modalities has provided conservators, scientists and art historians with the ability to examine the distribution of pigments across works of art with unprecedented detail. The labeled pigment maps produced were found to be robust within similar styles of paintings. Given that painting practices are relatively consistent within schools of artistic practices, we tested the suitability of using reflectance spectra from a subgroup of well-characterized paintings to build a large database to train a one-dimensional (spectral) convolutional neural network. For paintings, however, existing spectral databases are small and do not encompass the diversity encountered. ![]() Neural networks have been successful in modeling non-linear mixtures in remote sensing with large training datasets. Direct classification and labeling remain challenging because many paints are intimate pigment mixtures that require a non-linear unmixing model for a robust solution. In reflectance hyperspectral imaging, the data are classified into areas having similar spectra and turned into labeled pigment maps using spectral features and fusing with other information. Spectral imaging modalities, including reflectance and X-ray fluorescence, play an important role in conservation science. ![]()
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