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Journal articles on the topic 'Spectral analysis'

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Published: 4 June 2021
Last updated: 20 July 2024

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1

Guo, Hao, Kurt J. Marfurt, and Jianlei Liu. "Principal component spectral analysis." GEOPHYSICS 74, no. 4 (July 2009): P35—P43. http://dx.doi.org/10.1190/1.3119264.

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Spectral decomposition methods help illuminate lateral changes in porosity and thin-bed thickness. For broadband data, an interpreter might generate 80 or more somewhat redundant amplitude and phase spectral components spanning the usable seismic bandwidth at [Formula: see text] intervals. Large numbers of components can overload not only the interpreter but also the display hardware. We have used principal component analysis to reduce the multiplicity of spectral data and enhance the most energetic trends inside the data. Each principal component spectrum is mathematically orthogonal to other spectra, with the importance of each spectrum being proportional to the size of its corresponding eigenvalue. Principal components are ideally suited to identify geologic features that give rise to anomalous moderate- to high-amplitude spectra. Unlike the input spectral magnitude and phase components, the principal component spectra are not direct indicators of bed thickness. By combining the variability of multiple components, principal component spectra highlight stratigraphic features that can be interpreted using a seismic geomorphology workflow. By mapping the three largest principal components using the three primary colors of red, green, and blue, we could represent more than 80% of the spectral variance with a single image. We have applied and validated this workflow using a broadband data volume containing channels draining an unconformity, which was acquired over the Central Basin Platform, Texas, U.S.A. Principal component analysis reveals a channel system with only a few output data volumes. The same process provides the interpreter with flexibility to remove any unwanted high-amplitude geologic trends or random noise from the original spectral components by eliminating those principal components that do not aid in delineation of prospective features with their interpretation during the reconstruction process.
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2

Duan, Yanting, Chaodong Wu, Xiaodong Zheng, Yucheng Huang, and Jian Ma. "Coherence based on spectral variance analysis." GEOPHYSICS 83, no. 3 (May 1, 2018): O55—O66. http://dx.doi.org/10.1190/geo2017-0158.1.

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The eigenstructure-based coherence attribute is a type of efficient and mature tool for mapping geologic edges such as faults and/or channels in the 3D seismic interpretation. However, the eigenstructure-based coherence algorithm is sensitive to low signal-to-noise ratio seismic data, and the coherence results are affected by the dipping structures. Due to the large energy gap between the low- and high-frequency components, the low-frequency components play the principal role in coherence estimation. In contrast, the spectral variance balances the difference between the low- and high-frequency components at a fixed depth. The coherence estimation based on amplitude spectra avoids the effect of the time delays resulting from the dipping structures. Combining the spectral variance with the amplitude spectra avoids the effect of dipping structures and enhances the antinoise performance of the high-frequency components. First, we apply the short-time Fourier transform to obtain the time-frequency spectra of seismic data. Next, we compute the variance values of amplitude spectra. Then, we apply the fast Fourier transform to obtain the amplitude spectra of spectral variance. Finally, we calculate the eigenstructure coherence by using the amplitude spectra of spectral variance as the input. We apply the method to the theoretical models and practical seismic data. In the Marmousi velocity model, the coherence estimation using the amplitude spectra of the spectral variance as input shows more subtle discontinuities, especially in deeper layers. The results from field-data examples demonstrate that the proposed method is helpful for mapping faults and for improving the narrow channel edges’ resolution of interest. Therefore, the coherence algorithm based on the spectral variance analysis may be conducive to the seismic interpretation.
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Chen, Qiao, Li Jie Wang, and Stephen Westland. "Analysis of Hyperspectral Images Based on PCA." Advanced Materials Research 187 (February 2011): 641–46. http://dx.doi.org/10.4028/www.scientific.net/amr.187.641.

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Recent computational models of color vision demonstrate that it is possible to achieve exact color constancy over a limited range of lights and surfaces described by a low-dimensional linear model. For smooth reflectance spectra the different spectral bands have a significant degree of correlation. Any spectral reflectance distribution can be approximated to a specified degree of accuracy as a weighted sum of basis functions. Reflectance spectra of hyperspectral images of the natural scenes are supposed to represent the real world better than any certain classes of natural and man-made spectral reflectance data sets such as rocks, leaves, Munsell chips, etc. The characteristics of the spectra will be important to understand the spectral properties of the object reflectance and the representation of spectral images by linear models. In this study sets of hyperspectral images have been analyzed by principal-component analysis (PCA) method for spectral encoding.
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4

While, James, Andrew Jackson, Dirk Smit, and Ed Biegert. "Spectral analysis of gravity gradiometry profiles." GEOPHYSICS 71, no. 1 (January 2006): J11—J22. http://dx.doi.org/10.1190/1.2169848.

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The gravity gradient tensor (whose components are the second derivatives of the gravitational potential) is a symmetric tensor that, ignoring the constraint imposed by Laplace's equation, contains only six independent components. When measured on a horizontal plane, these components generate, in the spectral domain, six power spectral densities (PSDs) and fifteen cross-spectra. The cross-spectra can be split into two groups: a real group and a pure imaginary group. If the source distribution is statistically stationary, 1D spectra can be found from the 2D spectra via the slice theorem. The PSDs form two power-sum rules that link all gradient components. The power-sum rules, in combination with further equalities between the power and cross-spectra, reduce the number of independent spectra to 13, a number reduced to seven if the power spectrum of the potential is assumed isotropic. The power-sum rules, cross-spectral phases, and coherence between components all provide information on the internal consistency of a set of gradiometry measurements. This information can be used to assess the noise, to determine the isotropy, and, for a self-similar source, to calculate the scaling factor and average depth. When applied to a data set collected in the North Sea, the power-sum rules reveal high-frequency noise that is distributed among only three of the gradient components; additionally, the coherences reveal the source to be anisotropic with a nonzero correlation length.
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5

Chauhan, H., and B. Krishna Mohan. "Effectiveness of Spectral Similarity Measures to Develop Precise Crop Spectra for Hyperspectral Data Analysis." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 83–90. http://dx.doi.org/10.5194/isprsannals-ii-8-83-2014.

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The present study was undertaken with the objective to check effectiveness of spectral similarity measures to develop precise crop spectra from the collected hyperspectral field spectra. In Multispectral and Hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to select precise crop spectra from the set of field spectra. Conventionally crop spectra are developed after rejecting outliers based only on broad-spectrum analysis. Here a successful attempt has been made to develop precise crop spectra based on spectral similarity. As unevaluated data usage leads to uncertainty in the image classification, it is very crucial to evaluate the data. Hence, notwithstanding the conventional method, the data precision has been performed effectively to serve the purpose of the present research work. The effectiveness of developed precise field spectra was evaluated by spectral discrimination measures and found higher discrimination values compared to spectra developed conventionally. Overall classification accuracy for the image classified by field spectra selected conventionally is 51.89% and 75.47% for the image classified by field spectra selected precisely based on spectral similarity. KHAT values are 0.37, 0.62 and Z values are 2.77, 9.59 for image classified using conventional and precise field spectra respectively. Reasonable higher classification accuracy, KHAT and Z values shows the possibility of a new approach for field spectra selection based on spectral similarity measure.
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6

Xie, Busheng, Wenfei Mao, Boqi Peng, Shengyu Zhou, and Lixin Wu. "Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis." Minerals 12, no. 5 (April 20, 2022): 508. http://dx.doi.org/10.3390/min12050508.

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Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images.
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7

Clegg, Brian. "Spectral Analysis." Impact 2019, no. 1 (January 2, 2019): 12–16. http://dx.doi.org/10.1080/2058802x.2019.1589771.

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8

Morgan, Michael J. "Spectral analysis." Nature 375, no. 6527 (May 1995): 113–14. http://dx.doi.org/10.1038/375113a0.

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9

Sinha Roy, Aritro, Boris Dzikovski, Dependu Dolui, Olga Makhlynets, Arnab Dutta, and Madhur Srivastava. "A Simulation Independent Analysis of Single- and Multi-Component cw ESR Spectra." Magnetochemistry 9, no. 5 (April 23, 2023): 112. http://dx.doi.org/10.3390/magnetochemistry9050112.

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The accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understanding their structure–function relationships and electrochemical properties. The current methods of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly/partially resolved and unresolved spectra, which is common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper–nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.
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10

Dosiev, Anar. "Cartan–Slodkowski spectra, splitting elements and noncommutative spectral mapping theorems." Journal of Functional Analysis 230, no. 2 (January 2006): 446–93. http://dx.doi.org/10.1016/j.jfa.2005.03.014.

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11

He, Xing-Gang, Min-wei Tang, and Zhi-Yi Wu. "Spectral structure and spectral eigenvalue problems of a class of self-similar spectral measures." Journal of Functional Analysis 277, no. 10 (November 2019): 3688–722. http://dx.doi.org/10.1016/j.jfa.2019.05.019.

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12

Neal, Sharon L. "Comparison of Noniterative Factor Analysis Spectral Resolution Algorithms." Applied Spectroscopy 47, no. 8 (August 1993): 1161–68. http://dx.doi.org/10.1366/0003702934067766.

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The effects of fundamental and ancillary algorithm differences on the performance of three noniterative factor analysis spectral resolution algorithms on noisy and overlapped bilinear matrix-formatted spectral data are evaluated and compared. The evaluation consists of the analysis of simulated fluorescence excitation-emission matrices in which the spectral overlap, noise type, and level were systematically varied. The results indicate that the conventions used to exclude low-intensity, high-noise rows and columns from consideration as component spectra estimates and to choose the first estimates of the component spectra have significant impact on resolution algorithm performance. The results of the application of the algorithms to ideal data are nearly identical; however, there are several distinctions in the performance of the algorithms on noisy data. Verifiable estimates of the component spectra were resolved from data matrices degraded by white and Poisson noise that have signal-to-noise (S/N) ratios above 10 by all three algorithms regardless of the noise level and the degree of spectral overlap. The impact of pink noise was uniformly deleterious at S/N below 15.
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13

Bebekovska, Elena Vchkova, Galin Borisov, Zahary Donchev, and Gordana Apostolovska. "The latest spectral analysis on asteroids at NAO Rozhen." Proceedings of the International Astronomical Union 15, S350 (April 2019): 448–50. http://dx.doi.org/10.1017/s1743921319007610.

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AbstractBy virtue of the physical, chemical and dynamical characteristics of asteroids, researchers gain insight into the formation and evolution of our Solar system. Since these objects do not undergo any changes, or the changes during the Solar system evolution are insignificant, we are certain they carry important information regarding the formation of our planetary system and its evolution. Knowing the spectral class of an asteroid is crucial for determining its chemical properties. In our work the spectral classification was done on several asteroids by comparing their spectra with laboratory spectra. We determined spectral types of the asteroids by the overall shapes of the spectra between 450 nm and 700 nm. Increasing the number of asteroids with known rotation period, shapes and spectra enriches the asteroid database of physical and dynamical characteristics of asteroid population.
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14

da Cunha Lima, A. T., I. C. da Cunha Lima, and M. P. de Almeida. "Analysis of turbulence power spectra and velocity correlations in a pipeline with obstructions." International Journal of Modern Physics C 28, no. 02 (February 2017): 1750019. http://dx.doi.org/10.1142/s012918311750019x.

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We calculate the power spectral density and velocity correlations for a turbulent flow of a fluid inside a duct. Turbulence is induced by obstructions placed near the entrance of the flow. The power spectral density is obtained for several points at cross-sections along the duct axis, and an analysis is made on the way the spectra changes according to the distance to the obstruction. We show that the differences on the power spectral density are important in the lower frequency range, while in the higher frequency range, the spectra are very similar to each other. Our results suggest the use of the changes on the low frequency power spectral density to identify the occurrence of obstructions in pipelines. Our results show some frequency regions where the power spectral density behaves according to the Kolmogorov hypothesis. At the same time, the calculation of the power spectral densities at increasing distances from the obstructions indicates an energy cascade where the spectra evolves in frequency space by spreading the frequency amplitude.
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15

Jacques, Steven L., Ravikant Samatham, and Niloy Choudhury. "Rapid spectral analysis for spectral imaging." Biomedical Optics Express 1, no. 1 (July 15, 2010): 157. http://dx.doi.org/10.1364/boe.1.000157.

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16

Arun Prasad, K., and L. Gnanappazham. "Species Discrimination of Mangroves using Derivative Spectral Analysis." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 45–52. http://dx.doi.org/10.5194/isprsannals-ii-8-45-2014.

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Mangroves are salt tolerant trees or shrubs commonly seen in mudflats of intertidal regions of tropical and subtropical coastlines. Recent advances in field spectroscopic techniques enabled the species level discrimination among closely related vegetation types. In this study we have analysed the laboratory spectroscopy data collected from eight species of <i>Rhizophoraceaea</i> family of mangroves. The spectral data ranges between the wavelength of 350 nm and 2500 nm at a very narrow bandwidth of 1 nm. Preprocessing techniques including smoothing were done on the spectra to remove the noise before compiling it to a spectral library. Derivative analysis of the spectra was done and its corresponding first and second derivatives were obtained. Statistical analysis such as parametric and non-parametric tests were implemented on the original processed spectra as well as their respective first and second order derivatives for the identification of significant bands for species discrimination. Results have shown that red edge region (680 nm – 720 nm) and water vapour absorption region around 1150 nm and 1400 nm are optimal as they were consistent in discriminating species in reflectance spectra as well as in its first and second derivative spectra. C. <i>decandra</i> species is found to be discriminable from other species while reflectance and its derivative spectra were used. Non-parametric statistical analysis gave better results than that of parametric statistical analysis especially in SWIR 2 spectral region (1831 nm – 2500 nm).
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17

ZAKHAROVA, A. S., T. E. VADIVASOVA, and V. S. ANISHCHENKO. "SPECTRAL-CORRELATION ANALYSIS OF COUPLED CHAOTIC SELF-SUSTAINED OSCILLATORS." International Journal of Bifurcation and Chaos 18, no. 09 (September 2008): 2877–82. http://dx.doi.org/10.1142/s0218127408022147.

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Spectra and autocorrelation functions are studied in a system of two coupled self-sustained chaotic oscillators. We show that spectral and correlation characteristics are connected with partial effective phase diffusion coefficients. The evolution of spectra and diffusion coefficients are considered from the asynchronous regime to the regime of synchronous chaos. We draw the analogy between spectral characteristics of coupled chaotic oscillators and noisy coupled periodic oscillators.
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18

Barucci, M. A., P. H. Hasselmann, A. Praet, M. Fulchignoni, J. D. P. Deshapriya, S. Fornasier, F. Merlin, et al. "OSIRIS-REx spectral analysis of (101955) Bennu by multivariate statistics." Astronomy & Astrophysics 637 (May 2020): L4. http://dx.doi.org/10.1051/0004-6361/202038144.

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Contact. The NASA New Frontiers asteroid sample return mission Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) has provided a large amount of data on the asteroid (101955) Bennu, including high-quality spectra obtained by the OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS). Aims. To better constrain the surface properties and compositional variations of Bennu, we studied the visible and near-infrared spectral behavior across the asteroid surface by means of a statistical analysis aiming to distinguish spectrally distinct groups, if present. Methods. We applied the G-mode multivariate statistical analysis to the near-infrared OVIRS spectra to obtain an automatic statistical clustering at different confidence levels. Results. The statistical analysis highlights spectral variations on the surface of Bennu. Five distinct spectral groups are identified at a 2σ confidence level. At a higher confidence level of 3σ, no grouping is observed. Conclusions. The results at a 2σ confidence level distinguish a dominant spectral behavior group (group 1, background) and four small groups showing spectral slope variations, associated with areas with different surface properties. The background group contains most of the analyzed data, which implies a globally homogeneous surface at the spectral and spatial resolution of the data. The small groups with redder spectra are concentrated around the equatorial ridge and are associated with morphological surface features such as specific craters and boulders. No significant variation is detected in the band area or depth of the 2.74 μm band, which is associated with hydrated phyllosilicate content. The spectral slope variations are interpreted as a consequence of different regolith particle sizes, and/or porosity, and/or space weathering, that is, the presence of more or less fresh material. The OSIRIS-REx mission primary sampling site, Nightingale, and a boulder known as the Roc, are redder than the background surface.
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19

Barmpalias, Konstantinos G., Ndaona Chokani, Anestis I. Kalfas, and Reza S. Abhari. "Data Adaptive Spectral Analysis of Unsteady Leakage Flow in an Axial Turbine." International Journal of Rotating Machinery 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/121695.

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A data adaptive spectral analysis method is applied to characterize the unsteady loss generation in the leakage flow of an axial turbine. Unlike conventional spectral analysis, this method adapts a model dataset to the actual data. The method is illustrated from the analysis of the unsteady wall pressures in the labyrinth seal of an axial turbine. Spectra from the method are shown to be in good agreement with conventional spectral estimates. Furthermore, the spectra using the method are obtained with data records that are 16 times shorter than for conventional spectral analysis, indicating that the unsteady processes in turbomachines can be studied with substantially shorter measurement schedules than is presently the norm.
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20

Kupko, O. "Analysis of the Relationship Between Spectral and Color Uncertainty." Metrology and instruments, no. 5 (October 24, 2019): 20–26. http://dx.doi.org/10.33955/2307-2180(5)2019.20-26.

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An approach is proposed for an approximate estimate of the relation between the uncertainties of spectral and color measurements. The approach is based on replacing the entire diversity of the spectra with simple models: a constant over all wavelengths, a pronounced peak, a sharp decrease and a sharp increase. The Monte-Carlo method was used for calculations. Small distortions were introduced to the model spectra at each wavelength, the standard deviation of these distortions was calculated, after which the chromaticity coordinates were calculated in the system (x, y, Y). The value of Y, as related to light measurements, was not further analyzed. The standard deviation of the distances between the chromaticity coordinates of the initial spectrum and the chromaticity coordinates of the distorted spectra in space (x, y) was used as a measure of the uncertainty of color measurements. The ratio of these standard devia­tions was considered to be a link between the uncertainties of the spectral and color measurements. The influence of the uncertainties of the spectral measurements, the effect of the spectral width of the model spectra in the form of a peak, a sharp decrease and a sharp increase on the axis of the wavelengths were analyzed. The ran­ges of the corresponding values are chosen taking into account the valid documents and the most common spectra. The influence of a stan­dard illuminator selected was considered: all calculations were carried out for both type A and D65 sources. The results of the calculations are provided in the form of graphs, which allow one to approximately determine the required level of accuracy of the spectral equipment before the measurements, in order to provide the necessary uncertainty of chromaticity measurements. The analysis of the results obtained.
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21

Hoan, Duong Quoc, David P. Brown, Nguyen Dang Dat, Pham Thi Linh, and Cao Xuan Dinh. "MASS SPECTRAL ANALYSIS OF SOME ALLYLIC ESTERS." Journal of Science, Natural Science 60, no. 4 (2015): 3–8. http://dx.doi.org/10.18173/2354-1059.2015-00071.

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22

Hoan, Duong Quoc, David P. Brown, Nguyen Dang Dat, Pham Thi Linh, and Cao Xuan Dinh. "MASS SPECTRAL ANALYSIS OF SOME ALLYLIC ESTERS." Journal of Science, Natural Science 60, no. 4 (2015): 3–8. http://dx.doi.org/10.18173/2354-1075.2015-00071.

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23

Filipsky, Yu K., А. R. Аgadzhanyan, and I. V. Svyryd. "Application of time-frequency spectral analysis methods." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 1 (March 31, 2015): 141–45. http://dx.doi.org/10.15276/opu.1.45.2015.23.

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24

Khort, D. O., A. D. Chilikin, and B. M. Shurygin. "Analysis of hyperspectral images of apple fruits for automated sorting." Horticulture and viticulture, no. 6 (December 20, 2023): 41–50. http://dx.doi.org/10.31676/0235-2591-2023-6-41-50.

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One non-invasive method for detecting fruit damage involves monitoring them using hyper-spectrometers. Hyperspectral images comprise a set of spatially resolved radiation spectra of a reflected object. By using these data, it is possible to identify the characteristics and parameters of fruits that may indicate their damage. In this study, the spectral and spatial components of hyperspectral images of apple fruits were analysed. Random forest classifiers were used to detect objects in the images, with reflection spectra, vegetation indices and spatial texture descriptors (local binary patterns) used as input data for classifiers. Classifiers based on spectral characteristics proved to be more reliable than those trained without spectral data. Using spectral information about fruits signifi cantly improved the classifi cation results under the conditions of uneven lighting interference. By combining spectral data with machine learning methods, fruit sorting efficiency can be improved. This approach offers advanced development of methods for processing data from hyperspectral sensors installed on sorting lines in order to detect damage to apple fruits reliably
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25

Hrušková, M., I. Švec, and H. Sekerová. "Colour analysis and discrimination of laboratory prepared pasta by means of spectroscopic methods." Czech Journal of Food Sciences 29, No. 4 (August 10, 2011): 346–23. http://dx.doi.org/10.17221/25/2011-cjfs.

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For the CIE Lab colour profile determination of laboratory prepared pasta, two sample granulations and two spectral methods were tested. Pasta was manufactured progressively from semolina, common wheat, and corn flour. Sufficient colour spectra ranges were ensured by means of fortification with 9 non-traditional cereals in the first case, 8 natural colorants in the second one, and with 12 gluten-free pasta recipes in the last case. Both factors (i.e. granulation and spectral method) were proved as statistically significant by the cluster, variance and principal component analyses. In the comparison of the effects on the pasta composition and the spectral method, the latter demonstrated a stronger impact on the pasta colour profile measured.
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Pupyshev, A. A. "Spectral interferences and their correction in atomic emission spectral analysis." Industrial laboratory. Diagnostics of materials 85, no. 1II) (February 15, 2019): 15–32. http://dx.doi.org/10.26896/1028-6861-2019-85-1-ii-15-32.

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The main sources of spectral interferences in atomic emission spectral analysis (AESA) are considered, including both wide-range (bremsstrahlung and recombination continuum, radiation of hot condensed particles and electrode ends, scattered light in the spectrometer, overlapping of the analytical line by the wings of the neighbor strong spectral lines of interfering elements, imposition of the components of molecular bands with the very close lines) and narrow-band (partial or complete overlapping of the analytical line with atomic or ionic lines of the sample elements, electrodes and discharge atmosphere; superposition of spectra from higher orders of reflection in conventional diffraction spectrometers and from neighboring orders in two-dimensional echelle spectrometers). The features of their manifestation in various sources of spectrum excitation (flames, DC arc, spark discharges, arc plasma discharges, inductively coupled plasma, microwave plasma, low-pressure electric discharges, laser spark) are considered. The possibilities of reducing the level of spectral interferences or elimination of the spectral noise at the stage of design and manufacturing of AESA devices, as well as upon selecting and adjusting of operation conditions of the analysis are shown. Much attention is paid to the most easily implemented in practice off-peak correction of wide-range spectral interferences. The modern methods of background correction under the spectral peak (under-peak) using a software for atomic emission spectrometers and providing creation of various mathematical models of the background signal in the vicinity of the analytical line at the stage of developing a specific AESA technique are considered. The issues of the choice of spectral lines for analytical measurements, tables and atlases of spectral lines, electronic databases used for this purpose are considered in detail. Specific features of application of the method of inter-element correction with direct spectral overlapping of the lines are given. The operating sequence for taking into account spectral interferences when developing the analysis techniques is proposed.
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Dai, Xianzhe, and Weiping Zhang. "Higher Spectral Flow." Journal of Functional Analysis 157, no. 2 (August 1998): 432–69. http://dx.doi.org/10.1006/jfan.1998.3273.

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28

Wang, Fengfei, Ali Luo, and Haotong Zhang. "Error Analysis of SDSS/LAMOST Stellar Radial Velocity Measurement." Proceedings of the International Astronomical Union 9, S298 (May 2013): 444. http://dx.doi.org/10.1017/s1743921313007096.

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AbstractWe select super high quality spectra of spectral type A, F, G, K, M stars from SDSS C DR7. We mix our spectra with noise and then measure their radial velocity. The analysis shows that the accuracy of K, M type stars is much better than A, F stars. With our relationship between the signal-to-noise and accuracy of radial velocity measurement of different spectral type, we provide a good reference for stellar study related to radial velocity.
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Deng, Y., and C. Wu. "EXAMINATION OF SPECTRAL TRANSFORMATIONS ON SPECTRAL MIXTURE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 267–70. http://dx.doi.org/10.5194/isprs-archives-xlii-3-267-2018.

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While many spectral transformation techniques have been applied on spectral mixture analysis (SMA), few study examined their necessity and applicability. This paper focused on exploring the difference between spectrally transformed schemes and untransformed scheme to find out which transformed scheme performed better in SMA. In particular, nine spectrally transformed schemes as well as untransformed scheme were examined in two study areas. Each transformed scheme was tested 100 times using different endmember classes’ spectra under the endmember model of vegetation- high albedo impervious surface area-low albedo impervious surface area-soil (V-ISAh-ISAl-S). Performance of each scheme was assessed based on mean absolute error (MAE). Statistical analysis technique, Paired-Samples T test, was applied to test the significance of mean MAEs’ difference between transformed and untransformed schemes. Results demonstrated that only NSMA could exceed the untransformed scheme in all study areas. Some transformed schemes showed unstable performance since they outperformed the untransformed scheme in one area but weakened the SMA result in another region.
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Bai, Wurichaihu, Qingmei Bai, and Alatancang Chen. "Upper Semi-Weyl and Upper Semi-Browder Spectra of Unbounded Upper Triangular Operator Matrices." Journal of Function Spaces 2018 (November 25, 2018): 1–5. http://dx.doi.org/10.1155/2018/7871352.

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In this paper, we study the unbounded upper triangular operator matrix with diagonal domain. Some sufficient and necessary conditions are given under which upper semi-Weyl spectrum (resp. upper semi-Browder spectrum) of such operator matrix is equal to the union of the upper semi-Weyl spectra (resp. the upper semi-Browder spectra) of its diagonal entries. As an application, the corresponding spectral properties of Hamiltonian operator matrix are obtained.
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31

Cui, Hongwei, Haolei Zhang, Hao Ma, and Jiangtao Ji. "Research on SPAD Estimation Model for Spring Wheat Booting Stage Based on Hyperspectral Analysis." Sensors 24, no. 5 (March 6, 2024): 1693. http://dx.doi.org/10.3390/s24051693.

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With the rapid progression of agricultural informatization technology, the methodologies of crop monitoring based on spectral technology are constantly upgraded. In order to carry out the efficient, precise and nondestructive detection of relative chlorophyll (SPAD) during the booting stage, we acquired hyperspectral reflectance data about spring wheat vertical distribution and adopted the fractional-order differential to transform the raw spectral data. After that, based on correlation analysis, fractional differential spectra and fractional differential spectral indices with strong correlation with SPAD were screened and fused. Then, the least-squares support vector machine (LSSSVM) and the least-squares support vector machine (SMA-LSSSVM) optimized on the slime mold algorithm were applied to construct the estimation models of SPAD, and the model accuracy was assessed to screen the optimal estimation models. The results showed that the 0.4 order fractional-order differential spectra had the highest correlation with SPAD, which was 9.3% higher than the maximum correlation coefficient of the original spectra; the constructed two-band differential spectral indices were more sensitive to SPAD than the single differential spectra, in which the correlation reached the highest level of 0.724. The SMA-LSSSVM model constructed based on the two-band fractional-order differential spectral indices was better than the single differential spectra and the integration of both, which realized the assessment of wheat SPAD.
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32

Chertok, Michael, and Yosi Keller. "Spectral Symmetry Analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 7 (July 2010): 1227–38. http://dx.doi.org/10.1109/tpami.2009.121.

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33

Lee, Ann B., and Larry Wasserman. "Spectral Connectivity Analysis." Journal of the American Statistical Association 105, no. 491 (September 1, 2010): 1241–55. http://dx.doi.org/10.1198/jasa.2010.tm09754.

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Prothero, Jerrold, Zahidul Islam Islam, Henry Rodrigues, Luciano Mendes, Jon Gutiérrez, and Jon Montalban. "Instantaneous Spectral Analysis." Journal of Communication and Information Systems 34, no. 1 (2019): 12–26. http://dx.doi.org/10.14209/jcis.2019.2.

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35

Munz, F., V. Cunningham, M. May, S. Ziegler, S. Nekolla, M. Schwaiger, T. Ludwig, A. Bode, and P. Bartenstein. "Parallel Spectral Analysis." NeuroImage 7, no. 4 (May 1998): A29. http://dx.doi.org/10.1016/s1053-8119(18)31898-6.

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36

Jansen, Aren, and Partha Niyogi. "Intrinsic Spectral Analysis." IEEE Transactions on Signal Processing 61, no. 7 (April 2013): 1698–710. http://dx.doi.org/10.1109/tsp.2013.2238931.

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Kang, Hoon, and Joonsoo Ha. "Projection spectral analysis." International Journal of Control, Automation and Systems 13, no. 6 (September 26, 2015): 1530–37. http://dx.doi.org/10.1007/s12555-014-0286-y.

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38

Holser, Ronald A. "Principal Component Analysis of Phenolic Acid Spectra." ISRN Spectroscopy 2012 (October 18, 2012): 1–5. http://dx.doi.org/10.5402/2012/493203.

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Phenolic acids are common plant metabolites that exhibit bioactive properties and have applications in functional food and animal feed formulations. The ultraviolet (UV) and infrared (IR) spectra of four closely related phenolic acid structures were evaluated by principal component analysis (PCA) to develop spectral models for their rapid detection. Results demonstrated that UV and IR spectra could discriminate between each of the phenolic acids in overall models. Calculation of model scores and loadings showed that derivative UV spectra accounted for 99% variation with 2 principal components (PC) while derivative IR spectra required 3 PCs. Individual PCA models were developed for ferulic acid and p-coumaric acid using derivative UV spectra for detection and classification by soft independent modeling of class analogy (SIMCA). The application of this spectral technique as a classification model is expected to promote the use of agricultural residues as a source of these phenolic compounds.
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Albeverio, Sergio, Johannes Brasche, and Hagen Neidhardt. "On Inverse Spectral Theory for Self-Adjoint Extensions: Mixed Types of Spectra." Journal of Functional Analysis 154, no. 1 (April 1998): 130–73. http://dx.doi.org/10.1006/jfan.1997.3190.

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40

Burke, Harry B. "Proteomics: Analysis of Spectral Data." Cancer Informatics 1 (January 2005): 117693510500100. http://dx.doi.org/10.1177/117693510500100102.

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The goal of disease-related proteogenomic research is a complete description of the unfolding of the disease process from its origin to its cure. With a properly selected patient cohort and correctly collected, processed, analyzed data, large scale proteomic spectra may be able to provide much of the information necessary for achieving this goal. Protein spectra, which are one way of representing protein expression, can be extremely useful clinically since they can be generated from blood rather than from diseased tissue. At the same time, the analysis of circulating proteins in blood presents unique challenges because of their heterogeneity, blood contains a large number of different abundance proteins generated by tissues throughout the body. Another challenge is that protein spectra are massively parallel information. One can choose to perform top-down analysis, where the entire spectra is examined and candidate peaks are selected for further assessment. Or one can choose a bottom-up analysis, where, via hypothesis testing, individual proteins are identified in the spectra and related to the disease process. Each approach has advantages and disadvantages that must be understood if protein spectral data are to be properly analyzed. With either approach, several levels of information must be in tegrated into a predictive model. This model will allow us to detect disease and it will allow us to discover therapeutic interventions that reduce the risk of disease in at-risk individuals and effectively treat newly diagnosed disease.
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Costa-Mello, Daniel R., Simone Daflon, and Claudio B. Pereira. "Quantitative Spectral Analysis of hot Post-AGB Stars." Proceedings of the International Astronomical Union 5, S265 (August 2009): 356–57. http://dx.doi.org/10.1017/s1743921310000906.

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AbstractPost-AGB (PAGB) stars are luminous objects of low and intermediate mass in a final and short stage of evolution in the transition between AGB stars and planetary nebulae (PNe). In this work we present a quantitative spectral analysis of some hot PAGBs based on high resolution spectra. The stellar parameters and chemical composition were obtained from the synthesis of non-LTE spectra.
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42

Arsic, Branko, Dragos Cvetkovic, Slobodan Simic, and Milan Skaric. "Graph spectral techniques in computer sciences." Applicable Analysis and Discrete Mathematics 6, no. 1 (2012): 1–30. http://dx.doi.org/10.2298/aadm111223025a.

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We give a survey of graph spectral techniques used in computer sciences. The survey consists of a description of particular topics from the theory of graph spectra independently of the areas of Computer science in which they are used. We have described the applications of some important graph eigenvalues (spectral radius, algebraic connectivity, the least eigenvalue etc.), eigenvectors (principal eigenvector, Fiedler eigenvector and other), spectral reconstruction problems, spectra of random graphs, Hoffman polynomial, integral graphs etc. However, for each described spectral technique we indicate the fields in which it is used (e.g. in modelling and searching Internet, in computer vision, pattern recognition, data mining, multiprocessor systems, statistical databases, and in several other areas). We present some novel mathematical results (related to clustering and the Hoffman polynomial) as well.
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Pokern, Yvo, Benjamin Eltzner, Stephan F. Huckemann, Clemens Beeken, JoAnne Stubbe, Igor Tkach, Marina Bennati, and Markus Hiller. "Statistical analysis of ENDOR spectra." Proceedings of the National Academy of Sciences 118, no. 27 (July 2, 2021): e2023615118. http://dx.doi.org/10.1073/pnas.2023615118.

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Electron–nuclear double resonance (ENDOR) measures the hyperfine interaction of magnetic nuclei with paramagnetic centers and is hence a powerful tool for spectroscopic investigations extending from biophysics to material science. Progress in microwave technology and the recent availability of commercial electron paramagnetic resonance (EPR) spectrometers up to an electron Larmor frequency of 263 GHz now open the opportunity for a more quantitative spectral analysis. Using representative spectra of a prototype amino acid radical in a biologically relevant enzyme, the Y122• in Escherichia coli ribonucleotide reductase, we developed a statistical model for ENDOR data and conducted statistical inference on the spectra including uncertainty estimation and hypothesis testing. Our approach in conjunction with 1H/2H isotopic labeling of Y122• in the protein unambiguously established new unexpected spectral contributions. Density functional theory (DFT) calculations and ENDOR spectral simulations indicated that these features result from the beta-methylene hyperfine coupling and are caused by a distribution of molecular conformations, likely important for the biological function of this essential radical. The results demonstrate that model-based statistical analysis in combination with state-of-the-art spectroscopy accesses information hitherto beyond standard approaches.
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44

Berloff, P., and I. Kamenkovich. "On Spectral Analysis of Mesoscale Eddies. Part II: Nonlinear Analysis." Journal of Physical Oceanography 43, no. 12 (December 1, 2013): 2528–44. http://dx.doi.org/10.1175/jpo-d-12-0233.1.

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Abstract Several turbulent flow regimes of an idealized ocean circulation model are systematically analyzed in physical and spectral domains. Zonal dispersion properties of transient mesoscale eddies are described by the zonal wavenumber/temporal frequency spectra. The eddy patterns and the corresponding nonlinear eddy forcing exerted on the jets are examined by filtering different parts of the spectra. Comparison of the outcome of this nonlinear analysis with the properties of the linearized solutions (obtained in Part I of this paper) demonstrates that a very substantial part of the flow dynamics is controlled by the underlying linear dynamics. This result supports the hypothesis that it is possible to describe most of the oceanic mesoscale eddies as a wave turbulence phenomenon.
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JACQUES, STEVEN L. "SPECTRAL IMAGING AND ANALYSIS TO YIELD TISSUE OPTICAL PROPERTIES." Journal of Innovative Optical Health Sciences 02, no. 02 (April 2009): 123–29. http://dx.doi.org/10.1142/s1793545809000528.

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An introduction to the basics of spectral imaging as applied to biological tissues is presented. An example of a spectral image of a face is used to demonstrate the data and spectral analysis that specify the melanin content (M), blood content (B), tissue oxygen saturation (S), water content (W), fraction of scattering due to Rayleigh scattering (f) and due to Mie scattering (1 - f), and the reduced scattering coefficient at 500-nm wavelength (μ′s 500 nm). The sensitivity of reflectance spectra to variation in the various parameters is illustrated.
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46

Cui, Xiaoyu. "Water as a Probe for Standardization of Near-Infrared Spectra by Mutual–Individual Factor Analysis." Molecules 27, no. 18 (September 17, 2022): 6069. http://dx.doi.org/10.3390/molecules27186069.

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The standardization of near-infrared (NIR) spectra is essential in practical applications, because various instruments are generally employed. However, standardization is challenging due to numerous perturbations, such as the instruments, testing environments, and sample compositions. In order to explain the spectral changes caused by the various perturbations, a two-step standardization technique was presented in this work called mutual–individual factor analysis (MIFA). Taking advantage of the sensitivity of a water probe to perturbations, the spectral information from a water spectral region was gradually divided into mutual and individual parts. With aquaphotomics expertise, it can be found that the mutual part described the overall spectral features among instruments, whereas the individual part depicted the difference of component structural changes in the sample caused by operation and the measurement conditions. Furthermore, the spectral difference was adjusted by the coefficients in both parts. The effectiveness of the method was assessed by using two NIR datasets of corn and wheat, respectively. The results showed that the standardized spectra can be successfully predicted by using the partial least squares (PLS) models developed with the spectra from the reference instrument. Consequently, the MIFA offers a viable solution to standardize the spectra obtained from several instruments when measurements are affected by multiple factors.
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47

Goodrich, Robert K., and Karl E. Gustafson. "Spectral Approximation." Journal of Approximation Theory 48, no. 3 (November 1986): 272–93. http://dx.doi.org/10.1016/0021-9045(86)90051-1.

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48

Wu, Y., J. Xu, W. Yuan, H. Chen, and J. Bian. "Spectral analysis of 10-m resolution temperature profiles from balloon soundings over Beijing." Annales Geophysicae 24, no. 7 (August 11, 2006): 1801–8. http://dx.doi.org/10.5194/angeo-24-1801-2006.

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Abstract. Vertical temperature profiles with a height resolution of 10 m have been measured in the troposphere and lower stratosphere during March and April 2003 over the Beijing Meteorological Observatory. This resolution allows us to study temperature spectra up to higher wave numbers than many published papers. Our purposes in this study are to examine the spectral character of normalized temperature fluctuations in the 2.90–8.01 km (troposphere) and 14.65–19.76 km (lower stratosphere) altitude ranges and to compare them with model spectra. Vertical wave number spectra of six temperature profiles are presented. Results indicate that mean spectral slopes are about −1.9 in the troposphere and −2.2 in the lower stratosphere, which is believed to be the shallowest slopes ever measured by balloon-borne radiosonde soundings. Mean spectral amplitudes at m=1/(100 m) are about 17 times larger in the troposphere and 4 times larger in the lower stratosphere than the predicted saturated spectral amplitudes. These results show that the observed temperature spectra do not obey current gravity wave saturation models, the "universal" atmospheric spectrum model, or the wind-shifting model, in both slope and amplitude.
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Drees, L., and R. Roscher. "ARCHETYPAL ANALYSIS FOR SPARSE REPRESENTATION-BASED HYPERSPECTRAL SUB-PIXEL QUANTIFICATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1 (May 30, 2017): 133–39. http://dx.doi.org/10.5194/isprs-annals-iv-1-w1-133-2017.

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This paper focuses on the quantification of land cover fractions in an urban area of Berlin, Germany, using simulated hyperspectral EnMAP data with a spatial resolution of 30m&amp;times;30m. For this, sparse representation is applied, where each pixel with unknown surface characteristics is expressed by a weighted linear combination of elementary spectra with known land cover class. The elementary spectra are determined from image reference data using simplex volume maximization, which is a fast heuristic technique for archetypal analysis. In the experiments, the estimation of class fractions based on the archetypal spectral library is compared to the estimation obtained by a manually designed spectral library by means of reconstruction error, mean absolute error of the fraction estimates, sum of fractions and the number of used elementary spectra. We will show, that a collection of archetypes can be an adequate and efficient alternative to the spectral library with respect to mentioned criteria.
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Chu, Rui Jian, Noël Richard, Christine Fernandez-Maloigne, and Jon Yngve Hardeberg. "Relative Spectral Difference Occurrence Matrix: A Metrological Spectral-Spatial Feature for Hyperspectral Texture Analysis." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 386–92. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.69.

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We develop a spectral-spatial feature, Relative Spectral Difference Occurrence Matrix (RSDOM) for hyperspectral texture recognition. Inspired by Julesz's conjecture, the proposed feature is based on spectral difference approach and respects the metrological constraints. It simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. The feature is generic and adapted for any number of spectral bands and range. We validate our proposition by applying a classification scheme on the HyTexiLa database. An accuracy comparable to local binary pattern approach is obtained, but at a much reduced cost due to the extremely small feature size.
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