Hyperspectral imaging has become an increasingly used tool in the analysis of works of art. However, the quality of the acquired data and the processing of that data to produce accurate and reproducible spectral image cubes can be a challenge to many cultural heritage users. The calibration of data that is both spectrally and spatially accurate is an essential step in order to obtain useful and relevant results from hyperspectral imaging. Data that is too noisy or inaccurate will produce sub-optimal results when used for pigment mapping, the detection of hidden features, change detection or for quantitative spectral documentation. To help address this, therefore, we will examine the speci c acquisition and calibration work ows necessary for works of art. These work ows includes the key parameters that must be addressed during acquisition and the essential steps and issues at each of the stages required during post-processing in order to fully calibrate hyperspectral data. In addition we will look in detail at the key issues that a ect data quality and propose practical solutions that can make signi cant di erences to overall hyperspectral image quality.

An important application of imaging spectroscopy or hyperspectral imaging is the classification or discrimination of pigments based on the obtained spectral reflectance information. As opposed to being a point-analysis tool, this non-invasive method captures the entire surface of interest. This means that its potential is not only in the discrimination of pigments but also in their mapping. However, the challenge lies in the fact that in a real painting, there is no clear-cut edge between regions with certain pure pigments or of the exact same mixture. Pigments and other paint materials mix seamlessly and continuously in the physical domain. In this article, we introduce a divergence-based approach to pigment discrimination and mapping. The methodology is then applied to Munch's masterpiece The Scream (1893), whose pigments and materials have been identified for several points in the painting in a previous study. Through the introduced methodology, we have been able to extend the point analyzes of pigments and materials to the entire surface of the painting, recto and verso. RÉSUMÉUne importante application de la spectro-imagerie ou imagerie hyperspectrale est la classification ou la différenciation de pigments en fonction des données de réflectance spectrale obtenues. Contrairement à un instrument d'analyse ponctuel, cette méthode non invasive examine la surface d'intérêt dans son ensemble. Cela signifie que son potentiel n'est pas seulement la différenciation de pigments mais aussi leur cartographie. Cependant, la difficulté réside dans le fait que dans une véritable peinture, il n'y a pas de limite nette entre des zones de pigments purs ou de différents mélanges de ces pigments. Les pigments et autres matériaux constitutifs d'une peinture se mélangent imperceptiblement et continuellement dans le domaine physique. Dans cet article nous présentons une approche basée sur la divergence de spectre pour la différenciation des pigments et leur cartographie. Cette méthodologie est ensuite appliquée au chef-d'oeuvre de Munch Le Cri (1893), dont les pigments et matériaux constitutifs ont été identifiés en plusieurs points de la peinture dans une étude précédente. Grâce à la méthodologie proposée, nous avons pu étendre les analyses ponctuelles de pigments et autres matériaux à l'ensemble de la surface de la peinture, recto et verso. Traduit par Claire Cuyaubère. RESUMOUma aplicação importante da espectroscopia de imagem ou imagem hiperespectral é a classificação ou discriminação de pigmentos com base na informação de refletância espectral obtida. Ao contrário de ser uma ferramenta de análise pontual, esse método não invasivo captura toda a superfície de interesse. Isso significa que seu potencial não está apenas na discriminação de pigmentos, mas também em seu mapeamento. No entanto, o desafio reside no fato de que, em uma pintura real, não há uma borda nítida entre regiões com certos pigmentos puros ou que contenham exatamente a mesma mistura. Pigmentos e outros materiais de pintura se misturam perfeitamente e continuam...

The radiation captured in spectral imaging depends on both the complex light–matter interaction and the integration of the radiant light by the imaging system. In order to obtain material-specific information, it is important to define and invert an imaging process that takes into account both aspects. In this article, we investigate the use of several mixing models and evaluate their performances in the study of oil paintings. We propose an evaluation protocol, based on different features, i.e., spectral reconstruction, pigment mapping, and concentration estimation, which allows investigating the different properties of those mixing models in the context of spectral imaging. We conduct our experiment on oil-painted mockup samples of mixtures and show that models based on subtractive mixing perform the best for those materials.

Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results.

Photo-sensitive materials tend to change with exposure to light. Often, this change is visible when it affects the reflectance of the material in the visible range of the electromagnetic spectrum. In order to understand the photo-degradation mechanisms and their impact on fugitive materials, high-end scientific analysis is required. In a two-part article, we present a multi-modal approach to model fading effects in the spectral, temporal (first part) and spatial dimensions (second part). Specifically, we collect data from the same artwork, namely “A Japanese Lantern” by Norwegian artist, Oda Krohg, with two techniques, point-based microfading spectroscopy and hyperspectral imaging. In this first part, we focus on characterizing the pigments in the painting based on their spectral and fading characteristics. To begin with, using microfading data of a region in the painting, we analyze the color deterioration of the measured points. Then, we train a tensor decomposition model to reduce the measured materials to a spectral basis of unmixed pigments and, at the same time, to recover the fading rate of these endmembers (i.e. pure, unmixed chemical signals). Afterwards, we apply linear regression to predict the fading rate in the future. We validate the quality of these predictions by spectrally comparing them with temporal observations not included in the training part. Furthermore, we statistically assess the goodness of our model in explaining new data, collected from another region of the painting. Finally, we propose a visual way to explore the artist’s palette, where potential matches between endmembers and reference spectral libraries can be evaluated based on three metrics at once.