Spectral Smoothing Research Articles

Identification of Blueberry Beverage Using Vis/NIR Spectroscopy

Four kinds of blueberry beverage from different varieties, a total of 140 samples were acquired and analyzed by applying of spectrum technology. Using Savitzky-Golay spectral smoothing and multiplicative scatter correction (MSC) on the sample data pretreatment, four varieties of blueberry beverage were cluster analyzed by using principal component analysis method (PCA),a three-dimensional score view was achieved by the first 3 principal components of all samples (PC1, PC2 and PC3), which shows an obvious classification effect on the blueberry beverage. The first three principal components of the load diagram analysis, the characteristic bands related with the blueberry beverage varieties were 420-430nm, 490-500nm, 570-580nm and 1350-1365nm. According to the cumulative contribution rate (99.20%) of the first 6 principal components, the first 6 principal components was choosed as the input of multilayer perceptron (MLP) neural network, 100 samples in all the blueberry beverage samples were selected as a training set, and the remaining 40 samples were used as the prediction set. Training set were trained and prediction set were predicted by applying the multilayer perceptron neural network, and the correct rate of prediction were 100%.Research shows, using principal component analysis combined with multilayer perceptron neural network to identify the varieties of blueberry beverage is feasible.

Land Surface Temperature and Emissivity Separation from Cross-Track Infrared Sounder Data with Atmospheric Reanalysis Data and ISSTES Algorithm

The Cross-track Infrared Sounder (CrIS) is one of the most advanced hyperspectral instruments and has been used for various atmospheric applications such as atmospheric retrievals and weather forecast modeling. However, because of the specific design purpose of CrIS, little attention has been paid to retrieving land surface parameters from CrIS data. To take full advantage of the rich spectral information in CrIS data to improve the land surface retrievals, particularly the acquisition of a continuous Land Surface Emissivity (LSE) spectrum, this paper attempts to simultaneously retrieve a continuous LSE spectrum and the Land Surface Temperature (LST) from CrIS data with the atmospheric reanalysis data and the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm. The results show that the accuracy of the retrieved LSEs and LST is comparable with the current land products. The overall differences of the LST and LSE retrievals are approximately 1.3 K and 1.48%, respectively. However, the LSEs in our study can be provided as a continuum spectrum instead of the single-channel values in traditional products. The retrieved LST and LSEs now can be better used to further analyze the surface properties or improve the retrieval of atmospheric parameters.

A Spectral Emissivity Library of Spoil Substrates

Post-mining sites have a significant impact on surrounding ecosystems. Afforestation can restore these ecosystems, but its success and speed depends on the properties of the excavated spoil substrates. Thermal infrared remote sensing brings advantages to the mapping and classification of spoil substrates, resulting in the determination of its properties. A library of spoil substrates containing spectral emissivity and chemical properties can facilitate remote sensing activities. This study presents spectral library of spoil substrates’ emissivities extracted from brown coal mining sites in the Czech Republic. Extracted samples were homogenized by drying and sieving. Spectral emissivity of each sample was determined by spectral smoothing algorithm applied to data measured by a Fourier transform infrared (FTIR) spectrometer. A set of chemical parameters (pH, conductivity, Na, K, Al, Fe, loss on ignition and polyphenol content) and toxicity were determined for each sample as well. The spectral library presented in this paper also offers valuable information in the form of geographical coordinates for the locations where samples were obtained. Presented data are unique in nature and can serve many remote sensing activities in longwave infrared electromagnetic spectrum.

Evaluation of Temperature and Emissivity Retrieval using Spectral Smoothness Method for Low-Emissivity Materials

Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared (TIR) remote sensing. Along with the development of civil applications, increasing numbers of man-made low-emissivity materials can be found around our living environment. In addition, the characteristics and variation in properties of those materials should also be concerned. However, there are still few TES methods for low-emissivity materials reported in the literature. This paper addresses the performance of the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) method proposed by Borel (2008) for the retrieval of temperature and emissivity from hyperspectral TIR data for low-emissivity materials. The results show that those modeling errors are less than 0.11 K for temperature and 0.3% for emissivity as shown in the ARTEMISS algorithm if atmospheric parameters and the mean emissivity of material spectra are known. A sensitivity analysis has been performed, and the results show that the retrieval accuracy will be degraded with the increase of instrument noises, the errors of the atmospheric parameters, and the coarser spectral resolution. ARTEMISS can give a reasonable estimation of the temperature and emissivity for high- and low-emissivity materials; however, the performance of the algorithm is more seriously influenced by the atmospheric compensation than by the instrument noises. Our results show that the errors of temperature and emissivity become approximately three times than that when the instrument spectral properties are $1{\text{ cm}}^{-1}$ of sampling interval and $2{\text{ cm}}^{-1}$ of FWHM, and $4{\text{ cm}}^{-1}$ of sampling interval and $8{\text{ cm}}^{-1}$ of FWHM, respectively.

Plant species discrimination using emissive thermal infrared imaging spectroscopy

Discrimination of plant species in the optical reflective domain is somewhat limited by the similarity of their reflectance spectra. Spectral characteristics in the visible to shortwave infrared (VSWIR) consist of combination bands and overtones of primary absorption bands, situated in the Thermal Infrared (TIR) region and therefore resulting in broad spectral features. TIR spectroscopy is assumed to have a large potential for providing complementary information to VSWIR spectroscopy. So far, in the TIR, plants were often considered featureless. Recently and following advances in sensor technology, plant species were discriminated based on specific emissivity signatures by Ullah et al. (2012) using directional-hemispherical reflectance (DHR) measurements in the laboratory. Here we examine if an accurate discrimination of plant species is equally possible using emissive thermal infrared imaging spectroscopy, an explicit spatial technique that is faster and more flexible than non-imaging measurements.Hyperspectral thermal infrared images were acquired in the 7.8⿿11.56μm range at 40nm spectral resolution (@10μm) using a TIR imaging spectrometer (Telops HyperCam-LW) on seven plants each, of eight different species. The images were radiometrically calibrated and subjected to temperature and emissivity separation using a spectral smoothness approach. First, retrieved emissivity spectra were compared to laboratory reference spectra and then subjected to species discrimination using a random forest classifier. Second, classification results obtained with emissivity spectra were compared to those obtained with VSWIR reflectance spectra that had been acquired from the same leaf samples.In general, the mean emissivity spectra measured by the TIR imaging spectrometer showed very good agreement with the reference spectra (average Nash-Sutcliffe-Efficiency Index=0.64). In species discrimination, the resulting accuracies for emissivity spectra are highly dependent on the signal-to-noise ratio (SNR). At high SNR, the TIR data (Overall Accuracy (OAA)=92.26%) outperformed the VSWIR data (OAA=80.28%).This study demonstrates that TIR imaging spectroscopy allows for fast and spatial measurements of spectral plant emissivity with accuracies comparable to laboratory measurement. This innovative technique offers a valuable addition to VSWIR spectroscopy as it provides complimentary information for plant species discrimination.

Localization of the Reflection Sources of Stimulus-Frequency Otoacoustic Emissions.

The generation of stimulus-frequency otoacoustic emission (SFOAE) residuals in humans is analyzed both theoretically and experimentally to investigate the relation between the frequency difference between the probe and the suppressor tone and the localization of the residual source. Experimental measurements of the SFOAE residual were performed using suppressors of increasing frequency to separate the otoacoustic response from the probe stimulus. From the response to the probe alone, the SFOAE response was also estimated, using spectral smoothing, and compared with the residuals obtained for different frequency suppressors. A nonlinear delayed-stiffness active cochlear model was used to compute the spatial distribution of the residual sources according to a recent model of the local reflectivity from roughness, as a function of the suppressor frequency. The simulations clarified the role of high-frequency suppressors, showing that in humans, with increasing suppressor frequency, the generation region of the residual is only slightly basally shifted with respect to the case of a near-frequency suppressor, near the basal edge of the peak of the resonant basilar membrane response. As a consequence, the hierarchy among different-delay components correspondingly changes, gradually favoring short-delay components, with increasing suppressor frequency. Good agreement between the experimental and theoretical dependence of the level of otoacoustic components of different delay on the frequency shift between probe and suppressor confirms the validity of this interpretation.

Laser shock XAFS studies at OMEGA facility

ABSTRACTState-of-the-art laser facilities offer an excellent opportunity for studying materials at Mbar-Gbar pressures by dynamical compression. This paper summarizes recent experiments on EXAFS measurements of compressed solid iron up to 5 Mbar using OMEGA laser facility. The X-ray source is produced by a spherical implosion, providing enough brightness and spectral smoothness required for EXAFS measurements. The compression path is tuned by laser pulse shaping to achieve off-hugoniot states. With an anharmonic model, the density, temperature and upper limit of strength of the compressed iron are determined from EXAFS data. Prospects of XAFS study of other materials are also discussed.

Calibration requirements for detecting the 21 cm epoch of reionization power spectrum and implications for the SKA

21 cm Epoch of Reionization observations promise to transform our understanding of galaxy formation, but these observations are impossible without unprecedented levels of instrument calibration. We present end-to-end simulations of a full EoR power spectrum analysis including all of the major components of a real data processing pipeline: models of astrophysical foregrounds and EoR signal, frequency-dependent instrument effects, sky-based antenna calibration, and the full PS analysis. This study reveals that traditional sky-based per-frequency antenna calibration can only be implemented in EoR measurement analyses if the calibration model is unrealistically accurate. For reasonable levels of catalog completeness, the calibration introduces contamination in otherwise foreground-free power spectrum modes, precluding a PS measurement. We explore the origin of this contamination and potential mitigation techniques. We show that there is a strong joint constraint on the precision of the calibration catalog and the inherent spectral smoothness of antennae, and that this has significant implications for the instrumental design of the SKA and other future EoR observatories.

Research of beam conditioning technologies using continuous phase plate, Multi-FM smoothing by spectral dispersion and polarization smoothing

In the research of inertial confinement fusion, laser plasma interaction (LPI) is becoming a key problem that affects ignition. Here, multi-frequency modulation (Multi-FM) smoothing by spectral dispersion (SSD), continuous phase plate (CPP) and polarization smoothing (PS) were experimentally studied and implemented on the SG-III laser facility. After using these techniques, the far field distribution of SG-Ⅲ laser facility can be adjusted, controlled and repeated accurately. The output spectrums of the cascade phase modulators used for Multi-FM SSD were stable and the FM-to-AM effect can be restrained. Experiments on SG-III laser facility indicate that when the number of color cycles adopts 1, imposing SSD with 3.3 times diffraction limit (TDL) did not lead to pinhole closure in the spatial filters of preamplifier and main amplifiers with 30-TDL pinhole size. The nonuniformity of the focal spots using Multi-FM SSD, CPP and PS drops to 0.18, comparing to 0.26 with CPP+SSD, 0.57 with CPP+PS and 0.84 with only CPP and wedged lens. Polarization smoothing using flat birefringent plate in the convergent beam of final optics assembly (FOA) was studied. The PS plates were manufactured and equipped on SG-III laser facility for LPI research. Combined beam smoothing and polarization manipulation were also studied to solve the LPI problem. Results indicate that through adjusting dispersion directions of SSD beams in a quad, two dimensional SSD can be obtained. Using polarization control plate (PCP), polarization on the near field and far field can be manipulated, providing new method to solve LPI problem in indirect drive laser fusion.

Road surface layers geometric parameters estimation by ground penetrating radar using Estimation of Signal Parameters via Rotational Invariance Techniques method

In civil engineering, ground penetrating radar is widely used to assess the roadway structures. This study evaluates the influence of interface roughness. A modified estimation of signal parameters by rotational invariance techniques algorithm is proposed with a spectral smoothing technique for efficiently estimating the time delay, the permittivity of the layers and the interface roughness. To reduce the impact of noise, a preprocessing method called propagator is used to estimate the noise variance. The algorithm is tested on simulated data obtained by the rigorous numerical method called PILE (propagation inside layer expansion). Numerical simulations are conducted to assess the performance of the algorithm. The simulation results show that the proposed algorithm can estimate road parameters with small relative root mean square error.

Parametrizing Epoch of Reionization foregrounds: a deep survey of low-frequency point-source spectra with the Murchison Widefield Array

Experiments that pursue detection of signals from the Epoch of Reionization (EoR) are relying on spectral smoothness of source spectra at low frequencies. This article empirically explores the effect of foreground spectra on EoR experiments by measuring high-resolution full-polarization spectra for the 586 brightest unresolved sources in one of the MWA EoR fields using 45 h of observation. A novel peeling scheme is used to subtract 2500 sources from the visibilities with ionospheric and beam corrections, resulting in the deepest, confusion-limited MWA image so far. The resulting spectra are found to be affected by instrumental effects, which limit the constraints that can be set on source-intrinsic spectral structure. The sensitivity and power-spectrum of the spectra are analysed, and it is found that the spectra of residuals are dominated by PSF sidelobes from nearby undeconvolved sources. We release a catalogue describing the spectral parameters for each measured source.

A new scheme of spectral dispersion smoothing based on hybrid grating

The irradiance uniformity on target plane is a key issue in laser-driven inertial confinement facilities. In the typical schemes of one-dimensional smoothing by spectral dispersion (1D-SSD) and the “star” grating, the stripe pattern inside the focal spot appears inevitably, besides, the fabrication of the “star” grating is relatively difficult. Thus, a new spectral dispersion smoothing scheme based on a hybrid grating is proposed, which not only achieves the better smoothing effect, but also exhibits some specific advantages in the fabrication and the dispersion way. According to the different direction of the spectral dispersion, the hybrid grating is divided into inner and outer dispersion areas. That is, the dispersion direction of the inter dispersion area is in the horizontal or vertical direction, and the dispersion direction of the outer dispersion area is in the azimuthal direction. When the laser beam with the temporal phase modulation propagates through the hybrid grating, the dispersion directions of the laser beam in the inner and the outer dispersion areas are different, leading to the redistribution of the speckles inside the focal spot in the resultant direction of the translation and rotation on the target plane. Consequently, the focal spot on the target plane achieves the beam smoothing in radial and horizontal or vertical direction. In the present paper, the theoretical model of the hybrid grating scheme based on the spectral dispersion smoothing is built up. Using the theoretical model, the smoothing effect of the hybrid grating scheme is analyzed, and compared with those of the typical schemes of 1D-SSD and the “star” grating. The contrast and the fractional power above the intensity (FOPAI) are used to evaluate the smoothing characteristic of the focal spot. In addition, the influences of the area ratio and groove density in the different dispersion areas of the hybrid grating on beam-smoothing effect are also discussed. Results indicate that when the inner dispersion area accounts for the 0.3-0.5 of the total dispersion area, the hybrid grating scheme can effectively suppress the stripe intensity modulation both in the radial direction and the vertical direction. With increasing the groove densities of the inner and the outer dispersion area in a certain range, the irradiance uniformity of the focal spot is further improved. However, considering the actual processing of the hybrid grating, the appropriate groove density should be selected. Compared with the typical scheme of the 1D-SSD, the scheme of the hybrid grating can achieve the better smoothing effect with the multi-direction spectral dispersion smoothing. Furthermore, the fabrication of the hybrid grating is relatively simple and the irradiation uniformity on the target plane is also good compared with those of the “star” grating scheme.

Hyperspectral Remote Sensing Estimation Models for Pasture Quality

Crude protein (CP), crude fat (CFA) and crude fiber (CFI) are key indicators for evaluation of the quality and feeding value of pasture. Hence, identification of these biological contents is an essential practice for animal husbandry. As current approaches to pasture quality estimation are time-consuming and costly, and even generate hazardous waste, a real-time and non-destructive method is therefore developed in this study using pasture canopy hyperspectral data. A field campaign was carried out in August 2013 around Qinghai Lake in order to obtain field spectral properties of 19 types of natural pasture using the ASD Field Spec 3, a field spectrometer that works in the optical region (350-2 500 nm) of the electromagnetic spectrum. In additional to the spectral data, pasture samples were also collected from the field and examined in laboratory to measure the relative concentration of CP (%), CFA (%) and CFI (%). After spectral denoising and smoothing, the relationship of pasture quality parameters with the reflectance spectrum, the first derivatives of reflectance (FDR), band ratio and the wavelet coefficients (WCs) was analyzed respectively. The concentration of CP, CFA and CFI of pasture was found closely correlated with FDR with wavebands centered at 424, 1 668, and 918 nm as well as with the low-scale (scale = 2, 4) Morlet, Coiflets and Gassian WCs. Accordingly, the linear, exponential, and polynomial equations between each pasture variable and FDR or WCs were developed. Validation of the developed equations indicated that the polynomial model with an independent variable of Coiflets WCs (scale = 4, wavelength =1 209 nm), the polynomial model with an independent variable of FDR, and the exponential model with an independent variable of FDR were the optimal model for prediction of concentration of CP, CFA and CFI of pasture, respectively. The R2 of the pasture quality estimation models was between 0.646 and 0.762 at the 0.01 significance level. Results suggest that the first derivatives or the wavelet coefficients of hyperspectral reflectance in visible and near-infrared regions can be used for pasture quality estimation, and that it will provide a basis for real-time prediction of pasture quality using remote sensing techniques.

A New Music Instrument from Ancient Times: Modern Reconstruction of the Greek Lyre of Hermes using 3D Laser Scanning, Advanced Computer Aided Design and Audio Analysis

The current paper proposes a unique approach by examining the ancient Greek literature and antique black figure amphorae’s representations (also known as melanomorpha) for the reconstruction, after digital design processing, of a modern top-quality replica of an ancient tortoise lyre. Through the review of certain ancient Greek documents, the observation of the amphorae’s relevant representations, 3D scanning and reverse engineering as well as 3D design using advanced Computer Aided Design (CAD) software, this study illustrates the detailed drawings and the fabrication procedures followed for a modern version of the ancient musical instrument. By using only materials available in antiquity such as specific kinds of wood, tortoise carcasses and sheep strings, as well as modern carpentry technology, a high-quality musical instrument was produced suited for use by today’s professional musicians. Two variations were produced and tested utilizing the Phrygian and Lydian ancient Greek music scales in a specialized unechoic chamber, in order to define their sound properties. Typical statistics were computed in the frequency domain such as spectral centroid, spectral standard deviation, spectral skewness, spectral kurtosis along with spectral rolloff and spectral smoothness in order to justify the lyres’ quality as musical instruments. The result was a prototype music product using advanced 3D design procedures that can be produced in a rather repeatable manner.

High-order derivative spectrum in hydrothermally altered minerals discrimination

Minerals have unique spectral signatures that can be used for their identification similar to a fingerprint. Although some minerals have extremely similar compositions thus comparable signatures, they can be differentiated through remote sensing. Therefore, a derivative spectrum was used in this study, which enhanced the subtle spectral discrepancies to help determine whether a special order of a derivative spectrum is applicable in discrimination of these mineral targets. Second, we investigated whether derivative spectra in higher orders can be applied to discriminate between mineral targets using those in five orders as an input for a similarity measure (Jeffries-Matusita distance). Results of this study have shown that the best derivative order selection for each target is a target-specific problem. The first and fourth derivative orders were the bests for alunite and quartz minerals, respectively. As spectral smoothing is a preliminary process of derivative analysis, its bandwidth influence on derivative spectra was then investigated, and a smoothing window size of five sampling points was considered. Based on the results of this study, we recommend the introduction of high-order derivative spectra as the input for many detectors or classifiers in remote sensing especially for differentiation of minerals such as phengite, muscovite, and sericite.

FOREGROUND MODEL AND ANTENNA CALIBRATION ERRORS IN THE MEASUREMENT OF THE SKY-AVERAGED λ21 cm SIGNAL ATz∼ 20

The most promising near-term observable of the cosmic dark age prior to widespread reionization (z ∼ 15–200) is the sky-averaged λ21 cm background arising from hydrogen in the intergalactic medium. Though an individual antenna could in principle detect the line signature, data analysis must separate foregrounds that are orders of magnitude brighter than the λ21 cm background (but that are anticipated to vary monotonically and gradually with frequency, e.g., they are considered "spectrally smooth"). Using more physically motivated models for foregrounds than in previous studies, we show that the intrinsic spectral smoothness of the foregrounds is likely not a concern, and that data analysis for an ideal antenna should be able to detect the λ21 cm signal after subtracting a ∼fifth-order polynomial in log ν. However, we find that the foreground signal is corrupted by the angular and frequency-dependent response of a real antenna. The frequency dependence complicates modeling of foregrounds commonly based on the assumption of spectral smoothness. Our calculations focus on the Large-aperture Experiment to detect the Dark Age, which combines both radiometric and interferometric measurements. We show that statistical uncertainty remaining after fitting antenna gain patterns to interferometric measurements is not anticipated to compromise extraction of the λ21 cm signal for a range of cosmological models after fitting a seventh-order polynomial to radiometric data. Our results generalize to most efforts to measure the sky-averaged spectrum.

Foreground removal for Square Kilometre Array observations of the epoch of reionization with the correlated component analysis

We apply the Correlated Component Analysis (CCA) method on simulated data of the Square Kilometre Array, with the aim of accurately cleaning the 21 cm reionization signal from diffuse foreground contamination. The CCA has been developed for the Cosmic Microwave Background, but the application of the Fourier-domain implementation of this method to the reionization signal is straightforward. The CCA is a parametric method to estimate the frequency behaviour of the foregrounds from the data by using second-order statistics. We test its performance on foreground simulations of increasing complexity, designed to challenge the parametric models adopted. We also drop the assumption of spectral smoothness that most of the methods rely upon. We are able to clean effectively the simulated data across the explored frequency range (100-200 MHz) for all the foreground simulations. This shows that the CCA method is very promising for EoR component separation.

Assessment of Movement Patterns during Intubation between Novice and Experienced Providers Using Mobile Sensors: A Preliminary, Proof of Concept Study.

Background. There are likely marked differences in endotracheal intubation (ETI) techniques between novice and experienced providers. We performed a proof of concept study to determine if portable motion technology could identify the motion components of ETI between novice and experienced providers. Methods. We recruited a sample of novice and experienced providers to perform ETIs on a cadaver. Their movements during ETI were recorded with inertial measurement units (IMUs) on the left wrist. The signals were assessed visually between novice and experienced providers to identify areas of differences at key steps during ETI. We then calculated spectral smoothness (SS), a quantitative measure inversely related to movement variability, for all ETI attempts. Results. We enrolled five novice and five experienced providers. When visually inspecting the data, we noted maximum variability when inserting the blade of the laryngoscope into the mouth and while visualizing the glottic opening. Novice providers also had greater overall variability in their movement patterns (SS novice 6.4 versus SS experienced 26.6). Conclusion. Portable IMUs can be used to detect differences in movement patterns between novice and experienced providers in cadavers. Future ETI educational efforts may utilize portable IMUs to help accelerate the learning curve of novice providers.

Algorithms Research of near Infrared Spectral Database of Apples

A near infrared spectral database system of apples was studied. A new spectral matching algorithm based on Jaccard similarity coefficient (SMA-JSC) is proposed for higher spectral matching accuracy. A total of 840 apples of seven different varieties were used to evaluate the performance of this method. At the same time, a comparison was done among the existing spectral matching algorithms with spectral peak information (SMA-P) and the existing spectral matching algorithms with full spectra (SMA-FS). The highest accuracies of these existing SMA-P and SMA-FS were 48.9% and 72.57%, both of which were quite low mainly because of noise. For SMA-JSC, the first-order derivative was calculated and transformed into binary values (with only 0 or 1) to eliminate the influence of noise. The accuracy of our proposed algorithm was 94.1% for the calibration samples and 94.3% for the validation samples. Also, using linear discriminate analysis (LDA), we compared the existing SMA-P, the existing SMA-FS and our new proposed algorithm. The best result obtained using LDA was 88.0% (with the raw spectra) which was larger than the accuracies of the existing SMA-P and SMA-FS but less than the accuracy of our proposed SMA-JSC. In addition, several important spectral data parameters were optimised for automatic spectral peak detection without manually setting parameters (optimised parameters: spectral resolution was 32 cm−1, threshold of peak width was 29 spectral data points and threshold of peak shape index was 0.005). A selective spectral smoothing algorithm is also proposed to protect peak bands. With these methods, spectral peaks could be detected 100% correctly without manually setting parameters.

Total variation regularization via continuation to recover compressed hyperspectral images.

In this paper, we investigate a low-complexity scheme for decoding compressed hyperspectral image data. We have exploited the simplicity of the subgradient method by modifying a total variation-based regularization problem to include a residual constraint, employing convex optimality conditions to provide equivalency between the original and reformed problem statements. A scheme that utilizes spectral smoothness by calculating informed starting points to improve the rate of convergence is introduced. We conduct numerical experiments, using both synthetic and real hyperspectral data, to demonstrate the effectiveness of the reconstruction algorithm and the validity of our method for exploiting spectral smoothness. Evidence from these experiments suggests that the proposed methods have the potential to improve the quality and run times of the future compressed hyperspectral image reconstructions.