Hyperspectral Signatures Research Articles

Identification of mangrove plant using hyperspectral remote sensing data along the Red Sea, Egypt

The mangrove ecosystem is one of the most productive habitats that support many marine species and its adaptation to adverse environmental conditions, increase the demand to map, manage and monitor this ecosystem. The presence of hyperspectral remote sensing techniques can potentially improve the ability to measure the spectral signature of mangrove to differentiate mangrove from the other vegetation and to get detailed information about this ecosystem. This study has been carried out for mapping, monitoring and managing the Red Sea mangrove ecosystems through measuring their spectral properties using advanced hyperspectral remote sensing techniques. The spectral signature data were measured using Analytical Spectral Devices (ASD) Fieldspec spectroradiometer on November 2016 then the data were tested using statistical measures namely One-way Analysis of Variance (ANOVA) along with Tukey’s HSD test. The hyperspectral signatures of A. marina mangrove at the different sites showed that mangroves recorded a high reflectance at the visible and NIR region of the spectrum than the other regions and there are similarities at certain wavelengths and some differences at other wavelengths used for differentiation between mangroves in various environments. ANOVA and Tukey’s HSD test results showed that NIR region is the best region for the differentiation of mangrove from the other vegetation.

Soil water content measurement using hyper-spectral remote sensing techniques – A case study from north-western part of Tamil Nadu, India

This study examines the hyperspectral signatures (in the visible near-infrared (VNIR)–shortwave infrared (SWIR) regions of different samples of soils possessing varying water content. Totally 82 soil samples with differing water contents were examined using a hyperspectral radiometer operating in the 350–2,500-nm range, and the spectral curves were obtained. Statistical analyses (linear regression and Pearson R) of the curves indicate that overall reflectance increases with decreasing water content in the sample within the saturation limit. ‘Depth at 1400 nm region’ as a dependable spectral parameter since there exists a good correlation compared to other parameters. Further the depth of absorption at 1400 nm region correlates well with water content even when the water content crosses the saturation limit. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to estimate the water content of soils.

An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures

Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials’ signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene. Recently, investigating HU under the non-negligible endmember variability (EV) and outlier effects (OE) has drawn extensive attention. Some state-of-the-art works either consider EV or consider OE, but none of them considers both EV and OE simultaneously. In this paper, we propose a novel HU algorithm, referred to as the variability/outlier-insensitive multi-convex unmixing (VOIMU) algorithm, which is robust against both EV and OE. Considering two suitable regularizers, a nonconvex minimization problem is formulated for which the perturbed linear mixing model proposed by Thouvenin et al. , is used for modeling EV, while OE is implicitly handled by applying a $p$ quasi-norm to the data fitting with $0 . Then, we reformulate it into a multi-convex problem which is then solved by the block coordinate descent method, with convergence guarantee by casting it into the block successive upper bound minimization framework. The proposed VOIMU algorithm can yield a stationary-point solution with convergence guarantee, together with some intriguing information of potential outlier pixels though outliers are neither physically modeled in the above problem nor detected in the algorithm operation. Finally, we provide some simulation results and experimental results using real data to demonstrate the efficacy and practical applicability of the proposed VOIMU algorithm.

Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques

Accurate diagnosis of wheat leaf rust is of high interest for precision farming. Spectral data have been increasingly employed to detect this disease at leaf or canopy scales; however, less attention has been paid to the variations of leaf area index (LAI). Therefore, in this study, identification of wheat leaf rust was investigated at canopy scale and under different LAI levels, namely high, medium and low. Four machine learning (ML) methods including ν-support vector regression (ν-SVR), boosted regression trees (BRT), random forests regression (RFR) and Gaussian process regression (GPR) were built to estimate disease severity (DS) levels at canopy scale, where the reflectance data were measured in field situ by using a spectroradiometer, in which records spectra from 350 to 2500 nm. Results showed that ν-SVR outperformed the other ML methods at all three LAI levels with the R2 measures all being around 0.99. The results, particularly, showed that the performances of the ML methods were improved with increasing LAI value, where RFR reported the worst R2 value of 0.79 (RMSE = 8.5%) at low LAI level. The variable importance obtained using BRT showed three distinct regions of wavelengths that were appropriate across different LAI levels. The results of this research confirmed that hyperspectral signature can be reliably considered to identify wheat leaf rust disease at different LAI levels. Moreover, performances of several spectral vegetation indices (SVIs) were compared with those of the ML techniques. The results showed that the SVIs were consistently outperformed by the ML methods, particularly at low LAI level in which the SVIs were adversely affected. Nevertheless, all the SVIs, except for the RVSI, performed moderately well at high and medium LAI levels.

Estimating Nonlinearities in p-Linear Hyperspectral Mixtures

Accurately estimating the elements in Earth observations is crucial when assessing specific features such as air quality index, water pollution, or urbanization process behavior. Moreover, physical–chemical composition can be retrieved from hyperspectral images when proper spectral unmixing architectures are employed. Specifically, when linear and nonlinear combinations of endmembers (pure spectral components) are accurately characterized, hyperspectral unmixing plays a key role in understanding and quantifying phenomena occurring over the instantaneous field-of-view. Thus, reliable detection of nonlinear reflectance behavior can play a key role in enhancing hyperspectral unmixing performance. In this paper, two new methods for adaptive design of mixture models for hyperspectral unmixing are introduced. One of the methods relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers’ spectra. Then, an iterative process aims at understanding the order of local nonlinearity that is displayed by each endmember over every pixel. An improved version of an artificial neural network-based approach for nonlinearity order information is also considered and compared. Experimental results show that the proposed approaches are actually able to retrieve thorough information on the nature of the nonlinear effects over the image, while providing excellent performance in reconstructing the given data sets.

Evaluation of wavelengths and spectral reflectance indices for high-throughput assessment of growth, water relations and ion contents of wheat irrigated with saline water

Realistic simulations of saline field conditions and effective monitoring of phenotyping parameters in an expeditious, non-destructive manner are imperative to successful breeding of genotypes for salinity stress tolerance. This study aimed to spectrally assess the growth, water relations and ion contents of wheat under simulated saline field conditions using the subsurface water retention technique (SWRT) and three salinity water levels (control, 6, and 12 dS m−1). Phenotypic parameters and hyperspectral signatures of the canopy within the 350–2500 nm range were measured at the flowering stage. Multivariate analysis, including correlation, partial least squares regression, simultaneous b-coefficient and variable importance for projection (VIP), and stepwise multiple linear regression were used in the same order, to extract sensitive wavebands and effective singular wavelengths. Binary effective wavelengths as normalized spectral indices (NDSIs) were constructed and related to phenotypic parameters for pooled data and for each salinity level and cultivar. The results confirmed that the shoot dry weight (SDW), water relations and ion contents parameters were effective as screening criteria for evaluating the salt tolerance of wheat cultivars under simulated saline field conditions. It was possible to assess the phenotypic parameters by using hyperspectral canopy signatures over a broad spectrum range. All parameters exhibited stronger relationships with the wavelengths extracted in the visible-infrared (VIS) and red edge regions than those extracted in the near-infrared (NIR) and shortwave-infrared (SWIR) regions. Six wavelengths within the VIS region, five within the red edge and SWIR-1 regions, eleven within the NIR region, and nine within the SWIR-2 region were extracted as effective bands. The NDSIs based on VIS/VIS, red edge/red edge, red edge/VIS, NIR/VIS, NIR/red edge, and NIR/NIR were more appropriate for assessing the phenotypic parameters than indices based on SWIR/SWIR and SWIR/NIR, except for the SDW, K+ and Ca2+ contents, which showed strong correlations with the latter NDSIs. The close relationship between SDW and the water relations and ion contents parameters on one side and the high predictive power of the NDSIs based on the VIS, red edge, and NIR wavelengths in the assessment of phenotypic parameters on the other side indicates that the hyperspectral reflectance data and band selection techniques could be used for the indirect assessment of water relations and ion content of wheat under saline field conditions.

Detecting Signatures in Hyperspectral Image Data of Wounds: A Compound Model of Self- Organizing Map and Least Square Fitting

Abstract The purpose of this study is to develop a method to discriminate spectral signatures in wound tissue. We have collected a training set of the intensity of the remitted light for different types of wound tissue from different patients using a TIVITA™ tissue camera. We used a neural network technique (self-organizing map) to group areas with the same spectral properties together. The results of this work indicates that neural network models are capable of finding clusters of closely related hyperspectral signatures in wound tissue, and thus can be used as a powerful tool to reach the anticipated classification. Moreover, we used a least square method to fit literature spectra (i.e. oxygenated haemoglobin (O2Hb), deoxygenated haemoglobin (HHb), water and fat) to the learned spectral classes. This procedure enables us to label each spectral class with the corresponding absorbance properties for the different absorbance of interest (i.e. O2Hb, HHb, water and fat). The calculated parameters of a testing set were consistent with the expected behaviour and show a good agreement with the results of a second algorithm which is used in the TIVITA™ tissue camera.

Hyperspectral signature analysis using neural network for grade estimation of copper ore

The ever-increasing demand for the different metal and mineral resources from the earth’s subsurface has brought tremendous pressure on the geochemical laboratory for the growing countries. The success of any mining industry relies on the estimated values of ore grade in the mineral deposit. Hence, rapid assessment of ore grade is critical in daily schedule in mines operations. Commonly the assay value is determined by chemical analysis or X-Ray Fluorescence (XRF), which is one of the constrained by real-time grade estimation, duration of sample preparation and processing. Several researches carried out in exploration and revealed that hyperspectral technique is a promising tool for mineral identification and mapping. The goal of the present study is to determine the effectiveness of narrow band spectroscopy in Cu grade estimation. To achieve this, a multilayer feed-forward neural network model has been developed to establish a functional link between hyperspectral signature derived features with the copper grade. Altogether eight different types of features including absorption depth, band depth center, the area under the absorption curve, full width at half maxima were extracted from continuum removed spectra along with derivative reflectance features, e.g. band depth ratio, 1st and 2nd slopes from the hyperspectral profile. The dimensionality was reduced by applying Principal Component Analysis onto the extracted features. The first seven PCAs are then used as input vector of the ANN model. A five-fold cross-validation exercise is carried out for model performance. The high degree of correlation reveals that the PCA generated feature from hyperspectral data coupled with ANN model could be an alternative approach to predict the copper grade for the copper mine.

Exploring correlations between hyper-spectral signatures acquired in the laboratory and in-situ observation for heavy metal concentrations in soil

This study is a preliminary study for monitoring of heavy metal pollution in soil using airborne hyperspectral sensor. This paper presents an approach to exploring the correlation between the spectrum of heavy metals in soil from hyperspectral imagery taken in the laboratory and its observed concentration. In order to identify spectral wavelengths with a high correlation coefficient, hyperspectral data were collected from soil containing various concentrations (10–5000 ppm) of As, Cd, Cu, and Pb. Correlation analysis was performed with the hyperspectral imagery between the spectrum and its observed concentration obtained from instrumental analysis. The selective spectral wavelengths with a correlation coefficient (R2) above 0.80 will make a major contribution to study and monitoring of heavy metal pollution in soil using airborne hyperspectral sensor in the future. In order to accomplish desired result, further investigation is required.

Effect of linear and non-linear mixing on hyper-spectral signatures of snow in the optical region (350–2500 nm)

Study of hyper-spectral behaviour of snow is important to interpret, analyse and validate optical remote sensing observations. To map and understand response of snow-mixed pixels in RS data, field experiments were conducted for linear mixing of external materials (i.e. Vegetation, Soil) with snow, using spectral-radiometer (350–2500 nm). Further, systematic non-linear mixing of snow contaminants (soil, coal, ash) in terms of size and concentration of contaminants is analysed to imitate and understand spectral response of actual field scenarios. Sensitivity of band indices along with absorption peak characteristics provide clues to discriminate the type of contaminants. SWIR region is found to be useful for discriminating size of external contaminants in snow e.g. Avalanche deposited snow from light contaminated forms. Present research provide inputs for mapping snow-mixed pixels in medium/coarse resolution remote sensing RS data (in terms of linear mixing) and suitable wavelength selections for identification and discriminating type/size of snow contaminants (in terms of non-linear mixing).

Análisis Prospectivo de la Detección Hiperespectral de Cultivos de Arroz (Oryza Sativa L.)

The objective of this work is to perform a prospective analysis of the wavelengths that can be used to recognize a rice crop due to its phenological status and variety. For this purpose, field measurements of spectral signature in the 350 nm -1049 nm range were employed. The rice cultivars FCA 616FL and IDIAP 54-05 were used. The study site was located in the Juan Hombrón area in the Coclé province, Panama. A principal component analysis (PCA) was carried out, which resulted in the lengths 728.16, 677.89 and 785.48 nm let phenological differentiation within the cultivar FCA 616FL and IDIAP 54-05, the lengths 729.72, 814.58 and 666.81 nm let distinguish between crop varieties FCA 616FL and IDIAP 54-05 in vegetative phase.Keywords: Rice, reflectance, hyperspectral signature, phonological state.

Hyperspectral Signature Study Finds Corundum Alters to Diaspore Influence of Climate Change of Dharwar Craton Arsikere Band of Haranahalli, Hassan District, Karnataka, India

Hyperspectral Signature Study Finds Corundum Alters to Diaspore Influence of Climate Change of Dharwar Craton Arsikere Band of Haranahalli, Hassan District, Karnataka, India

Advanced Photogrammetry to Assess Lichen Colonization in the Hyper-Arid Namib Desert.

The hyper-arid central region of the Namib Desert is characterized by quartz desert pavement terrain that is devoid of vascular plant covers. In this extreme habitat the only discernible surface covers are epilithic lichens that colonize exposed surfaces of quartz rocks. These lichens are highly susceptible to disturbance and so field surveys have been limited due to concerns about disturbing this unusual desert feature. Here we present findings that illustrate how non-destructive surveys based upon advanced photogrammetry techniques can yield meaningful and novel scientific data on these lichens. We combined ‘structure from motion analysis,’ computer vision and GIS to create 3-dimensional point clouds from two-dimensional imagery. The data were robust in its application to estimating absolute lichen cover. An orange Stellarangia spp. assemblage had coverage of 22.8% of available substrate, whilst for a black Xanthoparmelia spp. assemblage coverage was markedly lower at 0.6% of available substrate. Hyperspectral signatures for both lichens were distinct in the near-infra red range indicating that Xanthoparmelia spp. was likely under relatively more moisture stress than Stellarangia spp. at the time of sampling, and we postulate that albedo effects may have contributed to this in the black lichen. Further transformation of the data revealed a colonization preference for west-facing quartz surfaces and this coincides with prevailing winds for marine fog that is the major source of moisture in this system. Furthermore, a three-dimensional ‘fly through’ of the lichen habitat was created to illustrate how the application of computer vision in microbiology has further potential as a research and education tool. We discuss how advanced photogrammetry could be applied in astrobiology using autonomous rovers to add quantitative ecological data for visible surface colonization on the surface of Mars.

Determination of Spectral Characteristics on Archaean Komatiites in Ghattihosahalli Schist Belt (Gsb) of Kumminagatta, Chitradurga District, Karnataka, India

Komatiites contain economic important nickel, chromium, titanium and copper deposits and their spectral absorption characters are highly important in the remote sensing technique to map and explore such deposits bearing rocks. This study integrates hyperspectral signatures; petrological and geochemical characters of serpentinite bearing ultramafic komatiite rock noticed at the Ghattihosahalli Schist Belt (GSB), near Kumminagatta village, in the Chitradurga district of Karnataka, India and demonstrate specific spectral absorptions of the rock. The measurement of spectral signatures of the rock using spectroradiometer produced significant absorptions near 700, 900 to1100, 1400, 2300, 2380 and 2470 nm in the 350-2500 nm wavelength. The spectral absorptions depend mainly on the optical and physico-chemical characters of the rock and are studied with the spectra of mineral library of USGS and JPL and characterized. This study can be used in the remote sensing technique to map similar rocks and bearing mineralization of the remote areas.

Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei.

Differences in early plant-pathogen interactions are mainly characterized by using destructive methods. Optical sensors are advanced techniques for phenotyping host-pathogen interactions on different scales and for detecting subtle plant resistance responses against pathogens. A microscope with a hyperspectral camera was used to study interactions between Blumeria graminis f. sp. hordei and barley (Hordeum vulgare) genotypes with high susceptibility or resistance due to hypersensitive response (HR) and papilla formation. Qualitative and quantitative assessment of pathogen development was used to explain changes in hyperspectral signatures. Within 48 h after inoculation, genotype-specific changes in the green and red range (500 to 690 nm) and a blue shift of the red-edge inflection point were observed. Manual analysis indicated resistance-specific reflectance patterns from 1 to 3 days after inoculation. These changes could be linked to host plant modifications depending on individual host-pathogen interactions. Retrospective analysis of hyperspectral images revealed spectral characteristics of HR against B. graminis f. sp. hordei. For early HR detection, an advanced data mining approach localized HR spots before they became visible on the RGB images derived from hyperspectral imaging. The link among processes during pathogenesis and host resistance to changes in hyperspectral signatures provide evidence that sensor-based phenotyping is suitable to advance time-consuming and cost-expensive visual rating of plant disease resistances.

Hyperspectral microscopy can detect metabolic heterogeneity within bovine post-compaction embryos incubated under two oxygen concentrations (7% versus 20%).

Can we separate embryos cultured under either 7% or 20% oxygen atmospheres by measuring their metabolic heterogeneity? Metabolic heterogeneity and changes in metabolic profiles in morula exposed to two different oxygen concentrations were not detectable using traditional fluorophore and two-channel autofluorescence but were detectable using hyperspectral microscopy. Increased genetic and morphological blastomere heterogeneity is associated with compromised developmental competence of embryos and currently forms the basis for embryo scoring within the clinic. However, there remains uncertainty over the accuracy of current techniques, such as PGS and time-lapse microscopy, to predict subsequent pregnancy establishment. The impact of two oxygen concentrations (7% = optimal and 20% = stressed) during post-fertilisation embryo culture was assessed. Cattle embryos were exposed to the different oxygen concentrations for 8 days (D8; embryo developmental competence) or 5 days (D5; metabolism measurements). Between 3 and 4 experimental replicates were performed, with 40-50 embryos per replicate used for the developmental competency experiment, 10-20 embryos per replicate for the fluorophore and two-channel autofluorescence experiments and a total of 21-22 embryos used for the hyperspectral microscopy study. In-vitro produced (IVP) cattle embryos were utilised for this study. Post-fertilisation, embryos were exposed to 7% or 20% oxygen. To determine impact of oxygen concentrations on embryo viability, blastocyst development was assessed on D8. On D5, metabolic heterogeneity was assessed in morula (on-time) embryos using fluorophores probes (active mitochondria, hydrogen peroxide and reduced glutathione), two-channel autofluorescence (FAD and NAD(P)H) and 18-channel hyperspectral microscopy. Exposure to 20% oxygen following fertilisation significantly reduced total blastocyst, expanded and hatched blastocyst rates by 1.4-, 1.9- and 2.8-fold, respectively, compared to 7% oxygen (P < 0.05), demonstrating that atmospheric oxygen was a viable model for studying mild metabolic stress. The metabolic profiles of D5 embryos was determined and although metabolic heterogeneity was evident within the cleavage stage (i.e. arrested) embryos exposed to fluorophores, there were no detectable difference in fluorescence intensity and pattern localisation in morula exposed to the two different oxygen concentrations (P > 0.05). While there were no significant differences in two-channel autofluorescent profiles of morula exposed to 7% and 20% oxygen (main effect, P > 0.05), morula that subsequently progressed to the blastocyst stage had significantly higher levels of FAD and NAD(P)H fluorescence compared to arrested morula (P < 0.05), with no change in the redox ratio. Hyperspectral autofluorescence imaging (in 18-spectral channels) of the D5 morula revealed highly significant differences in four features of the metabolic profiles of morula exposed to the two different oxygen concentrations (P < 0.001). These four features were weighted and their linear combination revealed clear discrimination between the two treatment groups. Metabolic profiles were assessed at a single time point (morula), and as such further investigation is required to determine if differences in hyperspectral signatures can be detected in pre-compaction embryos and oocytes, using both cattle and subsequently human models. Furthermore, embryo transfers should be performed to determine the relationship between metabolic profiles and pregnancy success. Advanced autofluorescence imaging techniques, such as hyperspectral microscopy, may provide clinics with additional tools to improve the assessment of embryos prior to transfer. This study was funded by the Australian Research Council Centre of Excellence for Nanoscale BioPhotonics (CE140100003). The Fluoview FV10i confocal microscope was purchased as part of the Sensing Technologies for Advanced Reproductive Research (STARR) facility, funded by the South Australian Premier's Science and Research Fund. The authors declare there are no conflict of interest.

Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection.

Hyperspectral images (HSIs) possess non-negative properties for both hyperspectral signatures and abundance coefficients, which can be naturally modeled using cone-based representation. However, in hyperspectral target detection, cone-based methods are barely studied. In this paper, we propose a new regularized cone-based representation approach to hyperspectral target detection, as well as its two working models by incorporating into the cone representation l2-norm and l1-norm regularizations, respectively. We call the new approach the matched shrunken cone detector (MSCD). Also important, we provide principled derivations of the proposed MSCD from the Bayesian perspective: we show that MSCD can be derived by assuming a multivariate half-Gaussian distribution or a multivariate half-Laplace distribution as the prior distribution of the coefficients of the models. In the experimental studies, we compare the proposed MSCD with the subspace methods and the sparse representation-based methods for HSI target detection. Two real hyperspectral data sets are used for evaluating the detection performances on sub-pixel targets and full-pixel targets, respectively. Results show that the proposed MSCD can outperform other methods in both cases, demonstrating the competitiveness of the regularized cone-based representation.

A Novel Preunmixing Framework for Efficient Detection of Linear Mixtures in Hyperspectral Images

In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of the geometrical properties of hyperspectral signatures. Experimental results on both real and synthetic data sets show that the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mixtures present in the pixels of the scene.

Reflectance Spectroscopy and Hyperspectral Imaging of Sapphire-Bearing Marble From the Beluga Occurrence, Baffin Island, Nunavut

Abstract Baffin Island (Nunavut, Canada) is located in the remote arctic where geological exposure is high, terrain is expansive, and field seasons are short. Metacarbonates in the Lake Harbour Group, southern Baffin Island, are considered to be prospective for sapphire (corundum), lapis lazuli (lazurite), and spinel gem deposits. This research is an investigation of the hyperspectral signature of rocks from the Beluga sapphire occurrence near Kimmirut, Nunavut. Thirty-five rock and thin section offcut samples from the Beluga corundum occurrence were studied in a laboratory setting using high spatial resolution imaging spectroscopy in the shortwave infrared region (SWIR, ∼975 to 2500 nm). Analysis of hyperspectral imagery successfully reproduced mineralogical and textural information relevant to gem mineralization previously assessed through thin section petrography and scanning electron microscopy. Scapolite, phlogopite, and muscovite from these rocks are shown to have distinct spectral responses from the host marble that is dominated by calcite. Furthermore, spectrally distinct prehnite and zeolite, both locally associated with corundum mineralization, are readily distinguishable. From the perspective of regional exploration, the small footprint of colored gemstone deposits in combination with their unique mineralogical makeup, unusual settings, and unfamiliarity to most geoscientists has resulted in very few discoveries in this remote but otherwise fertile area. These factors, in combination with the unique spectral responses observed from hand samples and thin section offcuts, provide a positive case for mapping and exploration of gem corundum targets by high spatial resolution hyperspectral imaging as part of regional surveys.

Rash diagnostics: an update on the diagnosis of allergic rashes.

The purpose of this review is to summarize recent research regarding the diagnosis of allergic rashes and to suggest future directions for the promotion of accurate diagnosis and endotype specification. Multiple cohort studies demonstrate that with appropriate clinical evaluation, drug allergy labels can be removed in up to 90% of cases. Genetic tests can predict severe adverse cutaneous drug reactions in some cases and in vitro tests are being sought to identify causative drugs in others. Biomarkers to define endotypes of atopic dermatitis are needed to predict which patients will benefit from evolving targeted therapies. Hyperspectral imaging is a rapidly evolving technology in medical diagnostics; additional research is needed to demonstrate whether this promising technology can be used to distinguish allergic rashes and/or endotypes in atopic dermatitis. Diagnostic tools for the assessment of allergic rashes are primitive in that they frequently rely on challenges to ascertain whether suspected allergens were causative. Validated in vitro tests with high sensitivity and specificity for drug allergies would benefit the field, particularly in delayed type reactions, as would identification of any hyperspectral signatures that could identify endotypes in atopic dermatitis.