Hyperspectral techniques have found application for the investigation of objects and samples in many scientific fields. Data evaluation approaches commonly either process them as stacks of randomly ordered spectra or as flat images. This thesis aims at combining both, lateral and spectral, aspects of hyperspectral data during data evaluation. Different approaches, such as lateral distance and neighbor pixel augmentation, will be explored with modern factorization techniques, such as t-stochastic neighbor embedding and self-organizing maps, and supported by artificial neural networks. The routines developed are applied on selected X-ray fluorescence data (XRF) sets from the field of materials science and cultural heritage. The altered augmented data set concept consists in augmenting the spectrum of a central pixel with the mean spectrum of its eight neighboring pixels. This new method is applied to the data set of a bi-modal Ti-6Al-6V-2Sn alloy. Clustering the optimized augmented data set with t-SNE reveals the existence of four components: phase with strong titanium signal, phase with strong vanadium signal, the first phase on top of the second phase and the second phase on top of the first phase. The later was not identified by clustering with t-SNE the data set augmented with all the spectra of its eight neighboring pixels. Clustering the XRF data set of a 13th century B.C. Egyptian mural painting with Self Organising Map (SOM) indicates areas with different thicknesses of copper and iron containing pigments. Clustering with Fast interpolation-based t-SNE (FIt-SNE) also reveals information about the painting sequence and the composition of a mixture of pigments. The pigments are not separable with a simple observation of the XRF elemental distribution images. Moreover, FIt-SNE dramatically accelerates t-SNE and can provide well-separated clusters. These properties are especially useful for large data sets, containing mixture of pigments. Finally, I introduce an optimised Artificial Neural Network (ANN) for training the photograph of a painting with the elemental distribution images, obtained by its XRF data set. The elemental distribution images are upscaled with Laplacian Pyramid Super-Resolution Network. In this way, we can identify with more accuracy the exact location of the damages and retouches on a 17th century A.C. easel painting (portrait of Hortense Manchini), since the result image is of high resolution.

The scientific analysis of historical paintings has been traditionally restricted to the analysis of paint cross-section samples. This invasive method provides extensive information but is inherently limited in scope due to the extreme heterogeneity of paintings. In the last decade, non-invasive spot analyses and spectral imaging methods have become increasingly widespread in cultural heritage science. Two of these methods are macroscopic X-ray fluorescence imaging spectroscopy (MA-XRF) and reflectance imaging spectroscopy (RIS). These methods allow for 2D-scanning the entire surface of a painting and provide complementary information on elemental and molecular composition and distribution of the paint. However, these methods are often used only for qualitative analysis of the paint based on relative distribution maps, revealing only limited information about the paint layer stratigraphy. This thesis is an exploration of a combined approach for quantitative analysis of paint composition and layer stratigraphy using MA-XRF and RIS. The research used a set of specially prepared paint samples of mixtures and multiple layer applications based on historically relevant pigments which were scanned using MA-XRF and RIS in the visible and Near IR range (400-2500 nm). The spectral data acquired were processed and analyzed in a variety of ways, including Non-negative Matrix Factorization, Non-Linear Least Square Fitting, among other methods; in an attempt to gather quantitative compositional and stratigraphic data. In these trials, using characteristic reflectance features in the visible range together with a comparison of highly and lightly absorbed X-ray fluorescence lines allowed for the identification and quantification of surface specific compounds related to the top paint layer. Further comparison of fluorescence lines and absorption features in the Near IR range provided a potential avenue for quantification of subsurface paint layers. The results confirm that the combination of these methods allows to reveal the paint stratigraphy. The project provides samples and data sets which may serve as the basis for the development of a robust algorithm to address this issue in the future.