Identification and mapping of ancient pigments in a Roman Egyptian funerary portrait by application of reflectance and luminescence imaging spectroscopy

Imaging spectroscopy (IS) is an important tool in the comprehensive technical analysis required of archaeological paintings. The complexity of pigment mixtures, diverse artistic practices and painting technologies, and the often-fragile and weathered nature of these objects render macroscale, non-invasive chemical mapping an essential component of the analytical protocol. Furthermore, the use of pigments such as Egyptian blue and madder lake, featuring diagnostic photoluminescence emission, provides motivation to perform photoluminescence mapping on the macroscale. This work demonstrates and advances new applications of dual-mode imaging spectroscopy and data analysis approaches for ancient painting. Both reflectance (RIS) and luminescence (LIS) modes were utilized for the study of a Roman Egyptian funerary portrait from second century CE Egypt. The first derivative of the RIS image cube was analyzed and found to significantly improve materials separation, identification, and the extent of mapping. Egyptian blue and madder lake were mapped across a decorated surface using their luminescence spectral signatures in the region of 540–1000 nm as endmembers in LIS analyses. Linear unmixing of the LIS endmembers and subsequent derivative analyses resulted in an improved separation and mapping of the luminescence pigments. RIS and LIS studies, combined with complementary, single-spot collection elemental and molecular spectroscopy, were able to successfully characterize the portrait’s painting materials and binding media used by the ancient artist, providing key insight into their material use, stylistic practices, and technological choices.

HyU: Hybrid Unmixing for longitudinal in vivo imaging of low signal to noise fluorescence

The expanded application of fluorescence imaging in biomedical and biological research towards more complex systems and geometries requires tools that can analyze a multitude of components at widely varying time- and length-scales. The major challenge in such complex imaging experiments is to cleanly separate multiple fluorescent labels with overlapping spectra from one another and background autofluorescence, without perturbing the sample with high levels of light. Thus, there is a requirement for efficient and robust analysis tools capable of quantitatively separating these signals. In response, we have combined multispectral fluorescence microscopy with hyperspectral phasors and linear unmixing to create Hybrid Unmixing (HyU). Here we demonstrate its capabilities in the dynamic imaging of multiple fluorescent labels in live, developing zebrafish embryos. HyU is more sensitive to low light levels of fluorescence compared to conventional linear unmixing approaches, permitting better multiplexed volumetric imaging over time, with less bleaching. HyU can also simultaneously image both bright exogenous and dim endogenous labels because of its high dynamic range. This allows studies of cellular behaviors, tagged components, and cell metabolism within the same specimen, offering a powerful window into the orchestrated complexity of biological systems.

Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee

As an invasive plant species, kudzu has been spreading rapidly in the Southeastern United States in recent years. Accurate mapping of kudzu is critical for effective invasion control and management. However, the remote detection of kudzu distribution using multispectral images is challenging because of the mixed reflectance and potential misclassification with other vegetation. We propose a three-step classification process to map kudzu in Knox County, Tennessee, using multispectral Sentinel-2 images and the integration of spectral unmixing analysis and phenological characteristics. This classification includes an initial linear unmixing process to produce an overestimated kudzu map, a phenological-based masking to reduce misclassification, and a nonlinear unmixing process to refine the classification. The initial linear unmixing provides high producer’s accuracy (PA) but low user’s accuracy (UA) due to misclassification with grasslands. The phenological-based masking increases the accuracy of the kudzu classification and reduces the domain for further processing. The nonlinear unmixing further refines the kudzu classification via the selection of an appropriate nonlinear model. The final kudzu classification for Knox County reaches relatively high accuracy, with UA, PA, Jaccard, and Kappa index values of 0.858, 0.907, 0.789, and 0.725, respectively. Our proposed method has potential for continuous monitoring of kudzu in large areas.

A Combined Unmixing Framework for Impervious Surface Mapping on Medium-Resolution Images with Visible Shadows

Spectral unmixing methods with medium-resolution remote sensing images have become the main approach to mapping urban impervious-surface information. However, as more tall buildings appear, numerous visible shadows exist in medium-resolution images; these have usually been ignored in previous research, but they seriously affect accuracy. To solve this problem, we propose a combined unmixing framework to extract impervious surface in nonshadow and shadow areas, using linear and nonlinear unmixing models, respectively. First shadow is separated from nonshadow. Then a nonlinear unmixing method is selected to map impervious surface in shadow, which is more suitable to the complex imaging environment in shadow, and a classic linear unmixing model in nonshadow. Through experimental tests, the proposed combined unmixing framework is shown to effectively reduce error in two study areas compared with classical unmixing methods.

Algoritmo de Descomposición Ciega Basado en el Modelo de Mezcla Multi-Lineal

Spectral unmixing has proven to be a great tool for the analysis of hyperspectral data, with linear mixing models (LMMs) being the most used in the literature. Nevertheless, due to the limitations of the LMMs to accurately describe the multiple light scattering effects in multi and hyperspectral imaging, new mixing models have emerged to describe nonlinear interactions. In this paper, we propose a new nonlinear unmixing algorithm based on a multilinear mixture model called Non-linear Extended Blind Endmember and Abundance Extraction (NEBEAE), which is based on a linear unmixing method established in the literature. The results of this study show that proposed method decreases the estimation errors of the spectral signatures and abundance maps, as well as the execution time with respect the state of the art methods.

Enfoque Híbrido para la Clasificación de Muestras Histológicas para la Detección de Tumores Cerebrales: Descomposición lineal y Redes Neuronales

Hyperspectral imaging has demonstrated its potential to provide information of the chemical composition of tissue and also of its morphological characteristics. However, discerning the presence of a pathology through this information is not a simple task. Because of this, a hybrid methodology is proposed in this work, which combines the identification of characteristic components present in a hyperspectral image from linear unmixing methods, and the ability to distinguish patterns from a neural network. The results of this research show that the proposed method can distinguish a tumor condition from histological brain samples with an average accuracy of 86%. The study demonstrates the potential of hybrid classification methodologies in the analysis of spectral information for the identification of histological samples affected by tumor tissue.