Hyperspectral Analysis Research Articles

Exploration of megapixel hyperspectral LIBS images using principal component analysis

A new methodology based on the well-known principal component analysis, designed for large, raw, and potentially complex dataset, is proposed for the multivariate hyperspectral analysis of LIBS images.

Hyperspectral core logging for fire reconstruction studies

Lacustrine sediments contain a wide range of proxies that enable paleoenvironmental reconstructions. For instance, charcoal can be used to document past fire regime changes. In order to analyse high-temporal- and spatial-resolution records, however, it is necessary to develop fast, low-cost and high-stratigraphic-resolution methods. We developed a new paleo-fire proxy by studying a lacustrine core from the Esterel Massif, SE France, an area affected by two recent fire events, in AD 1987 and 2003. For this purpose, we searched for charcoal deposited and preserved in the lake sediments by combining a number of complementary methods, including: classic macrocharcoal tallying, scanning spectrophotometry, scanning hyperspectral imaging and high pressure liquid chromatography analyses. Macrocharcoal quantification is efficient, but time-consuming, and only provides intermediate-resolution data (cm scale). Spectrophotometry, used classically to quantify colour, is very fast, provides high-resolution data (4 mm) and is non-destructive (core preservation). Hyperspectral data have the same advantages as spectrophotometry, but offer higher spatial resolution (64-µm pixel size) and higher spectral resolution (6 nm) for core logging applications. The main result of this research is based on hyperspectral analysis at very high stratigraphic resolution using the I-band index. This index usually measures reflectance values at [660, 670 nm] corresponding to the trough in red reflectance produced by Chlorophyll a and its diagenetic products. This [660, 670 nm] reflectance trough, however, is also affected by the presence of altered organic matter and decreases with altered organic matter such as charcoal particles. Charcoal effect on the reflectance of Chlorophyll a and its diagenetic products is identified on first derivative spectra by a characteristic pattern around 675 nm, which is also in agreement with the Chlorophyll a concentrations measured by high-pressure liquid chromatography and charcoal particles. The I-band index is hence suitable for detecting burned organic matter, by quantifying the dilution of the chlorophyll signal by the charcoal signal. Thus, this adaptation of the I-band index can be applied in fire reconstruction studies.

Noise-Resistant Spectral Features for Retrieving Foliar Chemical Parameters

Foliar chemical constituents are important indicators for understanding vegetation growing status and ecosystem functionality. Provided the noncontact and nondestructive traits, the hyperspectral analysis is a superior and efficient method for deriving these parameters. In practice, the spectral noise issue significantly impacts the performance of the hyperspectral retrieving system. To systematically investigate this issue, by introducing varying levels of noise to spectral signals, an assessment on noise-resistant capability of spectral features and models for retrieving concentrations of chlorophyll, carotenoids, and leaf water content was conducted. Given the continuous wavelet analysis (CWA) showed superior performance in extracting critical information associating plants biophysical and biochemical status in recent years, both wavelet features (WFs) and some conventional features (CFs) were chosen for the test. Two datasets including a leaf optical properties experiment dataset $(n\,= \,330)$ , and a corn leaf spectral experiment dataset $(n\,= \,213)$ were used for analysis and modeling. The results suggested that the WFs had stronger correlations with all leaf chemical parameters than the CFs. According to an evaluation by decay rate of retrieving error that indicates noise-resistant capability, both WFs and CFs exhibited strong resistance to spectral noise. Particularly for WFs, the noise-resistant capability is relevant to the scale of the features. Based on the identified spectral features, both univariate and multivariate retrieving models were established and achieved satisfactory accuracies. Synthesizing the retrieving accuracy, noise resistivity, and model's complexity, the optimal univariate WF-models were recommended in practice for retrieving leaf chemical parameters.

Insight into plant cell wall chemistry and structure by combination of multiphoton microscopy with Raman imaging.

Spontaneous Raman scattering microspectroscopy, second harmonic generation (SHG) and 2-photon excited fluorescence (2PF) were used in combination to characterize the morphology together with the chemical composition of the cell wall in native plant tissues. As the data obtained with unstained sections of Sorghum bicolor root and leaf tissues illustrate, nonresonant as well as pre-resonant Raman microscopy in combination with hyperspectral analysis reveals details about the distribution and composition of the major cell wall constituents. Multivariate analysis of the Raman data allows separation of different tissue regions, specifically the endodermis, xylem and lumen. The orientation of cellulose microfibrils is obtained from polarization-resolved SHG signals. Furthermore, 2-photon autofluorescence images can be used to image lignification. The combined compositional, morphological and orientational information in the proposed coupling of SHG, Raman imaging and 2PF presents an extension of existing vibrational microspectroscopic imaging and multiphoton microscopic approaches not only for plant tissues.

SOFTWARE FRAMEWORK FOR HYPERSPECTRAL DATA EXPLORATION AND PROCESSING IN MATLAB

Abstract. This paper presents a user introduction and a general overview of the MATLAB software package hsicube developed by the author for simplifying the data manipulation and visualization tasks often encountered in hyperspectral analysis work, and the design principles and software development methods used by the author. The framework implements methods for slicing, masking, visualization and application of existing functions to hyperspectral data cubes without the need to use explicit indexing or reshaping, as well as enabling expressive syntax for combining these operations on the command line for highly efficient data analysis workflows. It also includes utilities for interfacing with existing file reader scripts for easy access to files using the framework. The hsicube framework is released as open source to promote the free use and peer review of the code and enable collaborative development.

Online characterization of planetary surfaces: PlanetServer, an open-source analysis and visualization tool

The lack of open-source tools for hyperspectral data visualization and analysis creates a demand for new tools. In this paper we present the new PlanetServer, a set of tools comprising a web Geographic Information System (GIS) and a recently developed Python Application Programming Interface (API) capable of visualizing and analyzing a wide variety of hyperspectral data from different planetary bodies. Current WebGIS open-source tools are evaluated in order to give an overview and contextualize how PlanetServer can help in this matters. The web client is thoroughly described as well as the datasets available in PlanetServer. Also, the Python API is described and exposed the reason of its development. Two different examples of mineral characterization of different hydrosilicates such as chlorites, prehnites and kaolinites in the Nili Fossae area on Mars are presented. As the obtained results show positive outcome in hyperspectral analysis and visualization compared to previous literature, we suggest using the PlanetServer approach for such investigations.

Construction of smooth daily remote sensing time series data: a higher spatiotemporal resolution perspective

Research in time-series remote sensing data is receiving increasing attention. With the availability of relatively short repeat cycle and high spatial resolution satellite data, the construction and application of high spatiotemporal remote sensing time-series data is promising. In this paper, we proposed a method to construct complete spatial time series data, with Savitzky-Golay filter for smoothing and locally-adaptive linear interpolation for generating daily NDVI imagery. An IDL-based program was developed to achieve this goal. The China’s HJ-1 A/B satellite data were employed for this remote sensing time series construction. The results demonstrated that: (1) This method can generate smooth continuous time series image data successfully based on irregularly short-revisit remote sensing data; (2) HJ-1 A/B NDVI time-series were demonstrated to be successful in monitoring crop phenology and hyperspectral analysis was successfully applied on HJ-1 A/B time-series data to perform temporal endmember extraction. The IDL-based time-series construction program is generalizable for various kind of multi-temporal remote sensing data such as MODIS vegetation-index product. Discussion and concluding remarks are made to reveal the authors’ perspective on higher spatial resolution time-series analysis in the remote sensing community.

Hyperspectral analysis for extraction of chemical characteristics in dehydrated bones

Gelatin, a valuable commodity in food processing, pharmaceuticals and photography, is produced by boiling the connective tissues, bones and skins of animals. To be able to predict the quality of the resulting gelatin, a number of parameters, such as percentage of fat, protein, water and mineral content, are measured in the raw bones. We evaluate in this paper whether hyperspectral imaging can perform the required fast and accurate prediction of these parameters based on the spectral response of bone samples. This would allow replacing the time-consuming chemical analysis. The spectral response of nine different bone batches in the 600–1000 nm range (Vis-NIR) is correlated by means of Partial Least Square regression with the measured parameters. Our results show that high prediction accuracy can be obtained for all measured parameters based on the Vis-NIR spectral response. We can then conclude that hyperspectral imaging is a promising metric for the estimation of these chemical characteristics.

Total Variation Regularized Reweighted Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

Blind hyperspectral unmixing (HU), which includes the estimation of endmembers and their corresponding fractional abundances, is an important task for hyperspectral analysis. Recently, nonnegative matrix factorization (NMF) and its extensions have been widely used in HU. Unfortunately, most of the NMF-based methods can easily lead to an unsuitable solution, due to the nonconvexity of the NMF model and the influence of noise. To overcome this limitation, we make the best use of the structure of the abundance maps, and propose a new blind HU method named total variation regularized reweighted sparse NMF (TV-RSNMF). First, the abundance matrix is assumed to be sparse, and a weighted sparse regularizer is incorporated into the NMF model. The weights of the weighted sparse regularizer are adaptively updated related to the abundance matrix. Second, the abundance map corresponding to a single fixed endmember should be piecewise smooth. Therefore, the TV regularizer is adopted to capture the piecewise smooth structure of each abundance map. In our multiplicative iterative solution to the proposed TV-RSNMF model, the TV regularizer can be regarded as an abundance map denoising procedure, which improves the robustness of TV-RSNMF to noise. A number of experiments were conducted in both simulated and real-data conditions to illustrate the advantage of the proposed TV-RSNMF method for blind HU.

Hyperspectral analysis of cultural heritage artifacts: pigment material diversity in the Gough Map of Britain

The Gough Map, one of the earliest surviving maps of Britain, was created and extensively revised over the 15th century. In 2015, the map was imaged using a hyperspectral imaging system while in the collection at the Bodleian Library, Oxford University. The goal of the collection of the hyperspectral image (HSI) of the Gough Map was to address questions such as enhancement of faded text for reading and analysis of the pigments used during its creation and revision. In particular, pigment analysis of the Gough Map will help historians understand the material diversity of its composition and potentially the timeline of, and methods used in, the creation and revision of the map. Multiple analysis methods are presented to analyze a particular pigment in the Gough Map with an emphasis on understanding the within-material diversity, i.e., the number and spatial layout of distinct red pigments. One approach for understanding the number of distinct materials in a scene (i.e., endmember selection and dimensionality estimation) is the Gram matrix approach. Here, this method is used to study the within-material differences of pigments in the map with common visual color. The application is a pigment analysis tool that extracts visually common pixels (here, the red pigments) from the Gough Map and estimates the material diversity of the pixels. Results show that the Gough Map is composed of at least five kinds of dominant red pigments with a particular spatial pattern. This research provides a useful tool for historical geographers and cartographic historians to analyze the material diversity of HSI of cultural heritage artifacts.

Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data

Hyperspectral absorption features are important indicators of characterizing plant biophysical variables for the automatic diagnosis of crop diseases. Continuous wavelet analysis has proven to be an advanced hyperspectral analysis technique for extracting absorption features; however, specific wavelet features (WFs) and their relationship with pathological characteristics induced by different infestations have rarely been summarized. The aim of this research is to determine the most sensitive WFs for identifying specific pathological lesions from yellow rust and powdery mildew in winter wheat, based on 314 hyperspectral samples measured in field experiments in China in 2002, 2003, 2005, and 2012. The resultant WFs could be used as proxies to capture the major spectral absorption features caused by infestation of yellow rust or powdery mildew. Multivariate regression analysis based on these WFs outperformed conventional spectral features in disease detection; meanwhile, a Fisher discrimination model exhibited considerable potential for generating separable clusters for each infestation. Optimal classification returned an overall accuracy of 91.9% with a Kappa of 0.89. This paper also emphasizes the WFs and their relationship with pathological characteristics in order to provide a foundation for the further application of this approach in monitoring winter wheat diseases at the regional scale.

Hyperspectral remote sensing image retrieval system using spectral and texture features.

Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1)considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2)similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3)considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

Near-common-path interferometer for imaging Fourier-transform spectroscopy in wide-field microscopy.

Imaging Fourier-transform spectroscopy (IFTS) is a powerful method for biological hyperspectral analysis based on various imaging modalities, such as fluorescence or Raman. Since the measurements are taken in the Fourier space of the spectrum, it can also take advantage of compressed sensing strategies. IFTS has been readily implemented in high-throughput, high-content microscope systems based on wide-field imaging modalities. However, there are limitations in existing wide-field IFTS designs. Non-common-path approaches are less phase-stable. Alternatively, designs based on the common-path Sagnac interferometer are stable, but incompatible with high-throughput imaging. They require exhaustive sequential scanning over large interferometric path delays, making compressive strategic data acquisition impossible. In this paper, we present a novel phase-stable, near-common-path interferometer enabling high-throughput hyperspectral imaging based on strategic data acquisition. Our results suggest that this approach can improve throughput over those of many other wide-field spectral techniques by more than an order of magnitude without compromising phase stability.

Characterization of benign thyroid nodules with HyperSPACE (Hyper Spectral Analysis for Characterization in Echography) before and after percutaneous laser ablation: a pilot study

To evaluate the capability of the HyperSPACE (Hyper SPectral Analysis for Characterization in Echography)method in tissue characterization, in order to provide information for the laser treatment of benign thyroid nodules in respectof conventional B-mode images and elastography. The method, based on the spectral analysis of theraw radiofrequency ultrasonic signal, was applied to characterize the nodule before and after laser treatment. Thirty patients(25 females and 5 males, age between 37 and 81 years) with thyroid benign nodule at cytology (Thyr 2) were evaluated byconventional ultrasonography, elastography, and HyperSPACE, before and after laser ablation. The images processedby HyperSPACE exhibit different color distributions that are referred to different tissue features. By calculating the percentagesof the color coverages, the analysed nodules were subdivided into 3 groups. Each nodule belonging to the same groupexperienced, on average, similar necrosis extension. The nodules exhibit different Configurations (colors) distributions thatcould be indicative of the response of nodular tissue to the laser treatmentConclusions: HyperSPACEcan characterize benignnodules by providing additional information in respect of conventional ultrasound and elastography which is useful for supportin the laser treatment of nodules in order to increase the probability of success.

Microparticle Delivery of Cyclic AMP to Pulmonary Microvascular Endothelial Cells

Microparticles (MPs) are important for the exchange of information between cells. MPs are extracellular vesicles that range in size from 0.1 to 1 μm in diameter. They are constitutively released from many cell types and are thought to play a role in vascular homeostasis. Further, the number and content of MPs are altered in pathologies involving inflammation and vascular cell activation. MPs carry intracellular constituents from parent cells that affect target cell function. However, the mechanisms of delivery of the MP payload to target cells are unknown. Understanding the payload delivery system between MPs and target cells is the focus of this project.We recently discovered that cyclic adenosine monophosphate (cAMP), a ubiquitous second messenger, is found in MPs released from pulmonary microvascular endothelial cells (PMVECs). Additionally, MPs isolated from PMVECs treated with β adrenergic agonists and phosphodiesterase (PDE) inhibitors have increased cAMP levels. This suggests that different levels of cAMP are packaged in MPs from the parent cell. MPs containing maximal cAMP were therefore used to study the delivery of MP payload to target cells.We developed mathematical models of MP payload delivery to subcellular compartments of target PMVECs to better understand how the cAMP signaling pathway may be altered by MP‐mediated cAMP delivery. Simulations indicate that if the cAMP contained in one MP were released into a near‐membrane compartment of a target cell, cAMP levels within that compartment would be sufficient to activate protein kinase A (PKA) and Exchange protein activated by cAMP (Epac) for several minutes. Release of cAMP from a single MP released into the larger volume of the bulk cytoplasm would have little effect on target cell signaling. However, several MPs are often internalized by a target cell and their combined cAMP content would sufficiently elevate bulk cytosolic cAMP levels to activate PKA.To experimentally test the predictions of this model, MPs were collected from PMVECs treated with rolipram (a PDE type 4 inhibitor; 10 μM, 5 min) and isoproterenol (a β adrenergic agonist; 1 μM, 10 min). These MPs were added to cells transfected with a cytosolic cAMP FRET sensor. Time lapse hyperspectral image stacks were acquired using a Nikon A1R confocal microscope. Custom hyperspectral analysis scripts in MatLab (MathWorks) were used to unmix fluorescent spectra and quantify cAMP concentration changes throughout the target cell. Preliminary results indicate that MPs significantly increased cAMP levels in target cells compared to control. Together, these data indicate that MPs can deliver cAMP as a functional payload. Future studies will determine whether the MP‐delivered cAMP is sufficient to trigger sustained responses in target cells and contribute to MP‐mediated signaling events.Support or Funding InformationThis work was supported by NIH T32HL076125, P01HL066299, S10RR027535, and AHA 11SDG7390037.

Disruption of Autolysis in Bacillus subtilis using TiO2 Nanoparticles

In contrast to many nanotoxicity studies where nanoparticles (NPs) are observed to be toxic or reduce viable cells in a population of bacteria, we observed that increasing concentration of TiO2 NPs increased the cell survival of Bacillus subtilis in autolysis-inducing buffer by 0.5 to 5 orders of magnitude over an 8 hour exposure. Molecular investigations revealed that TiO2 NPs prevent or delay cell autolysis, an important survival and growth-regulating process in bacterial populations. Overall, the results suggest two potential mechanisms for the disruption of autolysis by TiO2 NPs in a concentration dependent manner: (i) directly, through TiO2 NP deposition on the cell wall, delaying the collapse of the protonmotive-force and preventing the onset of autolysis; and (ii) indirectly, through adsorption of autolysins on TiO2 NP, limiting the activity of released autolysins and preventing further lytic activity. Enhanced darkfield microscopy coupled to hyperspectral analysis was used to map TiO2 deposition on B. subtilis cell walls and released enzymes, supporting both mechanisms of autolysis interference. The disruption of autolysis in B. subtilis cultures by TiO2 NPs suggests the mechanisms and kinetics of cell death may be influenced by nano-scale metal oxide materials, which are abundant in natural systems.

Lookup-table-based inverse model for mapping oxygen concentration of cutaneous microvessels using hyperspectral imaging.

Hyperspectral imaging combining with skin optical clearing technique provides a possible way to non-invasively monitor hemodynamics of cutaneous microvessels. In order to estimate microvascular blood oxygen saturation, in this work, a lookup-table-based inverse model was developed to extract the microvascular optical and physiological properties using hyperspectral analysis. This approach showed a higher fitting degree than currently existing hyperspectral analysis methods (i.e. multiple linear regression and non-negative least square fit) in estimating blood oxygen saturation. Hypoxic stimulation experiment showed that calculated results were in accordance with physiological changes, and the relative changes of estimated oxygen saturation indicated this method appeared to be more sensitive to blood oxygen fluctuation. And a simulated blood model was used for verification here, indicating this method also showed a good accuracy in determining oxygen saturation from the simulated spectra.

Evaluation of enhanced darkfield microscopy and hyperspectral analysis to analyse the fate of silver nanoparticles in wastewaters

Enhanced darkfield microscopy coupled to hyperspectral analysis was evaluated for its capacity to detect Ag nanoparticles in wastewaters and biosolids.

Wheat Hardness Prediction Research Based on NIR Hyperspectral Analysis Combined with Ant Colony Optimization Algorithm

This paper presents a new and improved method that ant colony optimization (ACO) algorithm is combined with the support vector regression for band selection. The method is applied to the prediction research of wheat grain hardness, and tries to detect the feasibility of the forecasting ability. The optimized selection of characteristic wave band is the key link of the near infrared (NIR) hyperspectral analysis technology of wheat hardness. Experimental results showed that eleven characteristic wave band sub-intervals were selected from thirty spectral intervals by the algorithm, including 86 wave points. The selected wave band sub-interval were respectively 902.1 to 931.8 nm, 968.7 to 1027.5 nm, 1119.0 to 1143.4 nm, 1174.1 to 1275.5 nm, 1174.1 to 1275.5 nm, 1626.0 to 1647.6 nm and 1626.0 to 1647.6 nm. After using the optimized parameter in the spectral information forecasts and analyzes by the support vector regression. Prediction performances of regression models are assessed by calculating the estimated root mean square errors of cross-validation(RMSECV) the root mean square errors of prediction (RMSEP) and the correlation coefficient(R). The results showed that the estimated RMSECV and Rcv values were respectively 0.0382, and 0.9810 for the training set, the estimated RMSEP and RP values were respectively 0.0590, and 0.9544 for the validation set. Compared with the full spectrum of partial least squares (PLS), interval partial least squares (IPLS) algorithm, it simultaneously reduces the number of certain variables used in the model and increases in the prediction ability and the precision, and it can better reflect optimization model of the wave band. It is confirmed that the ACO method applied to the prediction research of the grain kernels is feasible.

Plasmonic labeling of subcellular compartments in cancer cells: multiplexing with fine-tuned gold and silver nanoshells.

Fine-tuned gold and silver nanoshells were produced via an entirely reformulated synthesis. The new method yielded ultramonodisperse samples, with polydispersity indexes (PI) as low as 0.02 and narrow extinction bands suited for multiplex analysis. A library of nanoshell samples with localized surface plasmon resonances (LSPR) spanning across the visible range was synthesized. Hyperspectral analysis revealed that the average scattering spectrum of 100 nanoshells matched closely to the spectrum of a single nanoshell, indicating an unprecedented low level of nanoparticle-to-nanoparticle variation for this type of system. A cell labeling experiment, targeting different subcellular compartments in MCF-7 human breast cancer cells, demonstrated that these monodisperse nanoparticles can be used as a multiplex platform for single cell analysis at the intracellular and extracellular level. Antibody-coated gold nanoshells targeted the plasma membrane, while silver nanoshells coated with a nuclear localization signal (NLS) targeted the nuclear membrane. A fluorescence counterstaining experiment, as well as single cell hyperspectral microscopy showed the excellent selectivity and specificity of each type of nanoparticle for its designed subcellular compartment. A time-lapse photodegradation experiment confirmed the enhanced stability of the nanoshells over fluorescent labeling and their capabilities for long-term live cell imaging.