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Journal articles on the topic 'Multi-spectral method'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Wu, Renjie, Yuqi Li, Xijiong Xie, and Zhijie Lin. "Optimized Multi-Spectral Filter Arrays for Spectral Reconstruction." Sensors 19, no. 13 (June 30, 2019): 2905. http://dx.doi.org/10.3390/s19132905.

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Multispectral filter array (MSFA)-based imaging is a compact, practical technique for snapshot spectral image capturing and reconstruction. The imaging and reconstruction quality is highly influenced by the spectral sensitivities and spatial arrangement of channels on MSFAs, and the used reconstruction method. In order to design a MSFA with high imaging capacity, we propose a sparse representation based approach to optimize spectral sensitivities and spatial arrangement of MSFAs. The proposed approach first overall models the various errors associated with spectral reconstruction, and then uses a global heuristic searching method to optimize MSFAs via minimizing the estimated error of MSFAs. Our MSFA optimization method can select filters from off-the-shelf candidate filter sets while assigning the selected filters to the designed MSFA. Experimental results on three datasets show that the proposed method is more efficient, flexible, and can design MSFAs with lower spectral construction errors when compared with existing state-of-the-art methods. The MSFAs designed by our method show better performance than others even using different spectral reconstruction methods.

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2

Fatih Talu, Muhammed. "Multi-level spectral graph partitioning method." Journal of Statistical Mechanics: Theory and Experiment 2017, no. 9 (September 27, 2017): 093406. http://dx.doi.org/10.1088/1742-5468/aa85ba.

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3

MOCHIZUKI, Kosuke, Norihiro TANAKA, Kazunari HAYASHI, Jae-Yong WOO, and Shoji TOMINAGA. "A Resolution Improvement Method for Multi-spectral Rendering Based on Multi-spectral Image Compression." Transactions of Japan Society of Kansei Engineering 9, no. 2 (2010): 301–9. http://dx.doi.org/10.5057/jjske.j11-090520-24.

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4

Speck, Robert, Daniel Ruprecht, Matthew Emmett, Michael Minion, Matthias Bolten, and Rolf Krause. "A multi-level spectral deferred correction method." BIT Numerical Mathematics 55, no. 3 (August 26, 2014): 843–67. http://dx.doi.org/10.1007/s10543-014-0517-x.

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5

YANG Ying, 杨鹰, 孔玲君 KONG Ling-jun, and 刘真 LIU Zhen. "Multi-spectral demosaicking method based on compressive sensing." Chinese Journal of Liquid Crystals and Displays 32, no. 1 (2017): 56–61. http://dx.doi.org/10.3788/yjyxs20173201.0056.

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6

Ma, Cui, Hui Lin, Guodong Zhang, and Ruxu Du. "An efficient calibration method for multi-spectral imaging." Optics Communications 420 (August 2018): 14–25. http://dx.doi.org/10.1016/j.optcom.2018.03.025.

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7

SHOKROLAHIZADEH, B., and H. SHODJA. "Spectral equivalent inclusion method: Anisotropic cylindrical multi-inhomogeneities." Journal of the Mechanics and Physics of Solids 56, no. 12 (December 2008): 3565–75. http://dx.doi.org/10.1016/j.jmps.2008.04.008.

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Li, Sheng, MingXi Wan, and SuPin Wang. "Multi-Band Spectral Subtraction Method for Electrolarynx Speech Enhancement." Algorithms 2, no. 1 (March 13, 2009): 550–64. http://dx.doi.org/10.3390/a2010550.

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Iskandarani, M., D. B. Haidvogel, J. C. Levin, E. Curchitser, and C. A. Edwards. "Multi-scale geophysical modeling using the spectral element method." Computing in Science & Engineering 4, no. 5 (September 2002): 42–48. http://dx.doi.org/10.1109/mcise.2002.1032428.

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Daigo, M., A. Ono†, R. Urabe‡, and N. Fujiwara. "Pattern decomposition method for hyper-multi-spectral data analysis." International Journal of Remote Sensing 25, no. 6 (March 2004): 1153–66. http://dx.doi.org/10.1080/0143116031000139872.

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Rigny, A., A. Bruno, and H. Sik. "Multigrating method for flattened spectral response wavelength multi/demultiplexer." Electronics Letters 33, no. 20 (1997): 1701. http://dx.doi.org/10.1049/el:19971146.

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12

Guo, Yi-Nan, Dawei Xiao, Jian Cheng, and Yuanshun Zhu. "ISKC Classification Method for Multi-Spectral Remote Sensing Images." Journal of Nanoelectronics and Optoelectronics 7, no. 2 (March 1, 2012): 177–80. http://dx.doi.org/10.1166/jno.2012.1240.

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13

GHOREISHI, F., and P. MOKHTARY. "SPECTRAL COLLOCATION METHOD FOR MULTI-ORDER FRACTIONAL DIFFERENTIAL EQUATIONS." International Journal of Computational Methods 11, no. 05 (October 2014): 1350072. http://dx.doi.org/10.1142/s0219876213500722.

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In this paper, the spectral collocation method is investigated for the numerical solution of multi-order Fractional Differential Equations (FDEs). We choose the orthogonal Jacobi polynomials and set of Jacobi Gauss–Lobatto quadrature points as basis functions and grid points respectively. This solution strategy is an application of the matrix-vector-product approach in spectral approximation of FDEs. The fractional derivatives are described in the Caputo type. Numerical solvability and an efficient convergence analysis of the method have also been discussed. Due to the fact that the solutions of fractional differential equations usually have a weak singularity at origin, we use a variable transformation method to change some classes of the original equation into a new equation with a unique smooth solution such that, the spectral collocation method can be applied conveniently. We prove that after this regularization technique, numerical solution of the new equation has exponential rate of convergence. Some standard examples are provided to confirm the reliability of the proposed method.

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14

Gao, Shan, Chi Feng, Lixin Wang, and Dong Li. "Multi-spectral temperature measurement method for gas turbine blade." Optical Review 23, no. 1 (November 26, 2015): 17–25. http://dx.doi.org/10.1007/s10043-015-0155-9.

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15

Lu, Liang, Hongbao Zhu, Junyu Dong, Yakun Ju, and Huiyu Zhou. "Three-Dimensional Reconstruction with a Laser Line Based on Image In-Painting and Multi-Spectral Photometric Stereo." Sensors 21, no. 6 (March 18, 2021): 2131. http://dx.doi.org/10.3390/s21062131.

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This paper presents a multi-spectral photometric stereo (MPS) method based on image in-painting, which can reconstruct the shape using a multi-spectral image with a laser line. One of the difficulties in multi-spectral photometric stereo is to extract the laser line because the required illumination for MPS, e.g., red, green, and blue light, may pollute the laser color. Unlike previous methods, through the improvement of the network proposed by Isola, a Generative Adversarial Network based on image in-painting was proposed, to separate a multi-spectral image with a laser line into a clean laser image and an uncorrupted multi-spectral image without the laser line. Then these results were substituted into the method proposed by Fan to obtain high-precision 3D reconstruction results. To make the proposed method applicable to real-world objects, a rendered image dataset obtained using the rendering models in ShapeNet has been used for training the network. Evaluation using the rendered images and real-world images shows the superiority of the proposed approach over several previous methods.

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Piao, Jingchun, Yunfan Chen, and Hyunchul Shin. "A New Deep Learning Based Multi-Spectral Image Fusion Method." Entropy 21, no. 6 (June 5, 2019): 570. http://dx.doi.org/10.3390/e21060570.

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In this paper, we present a new effective infrared (IR) and visible (VIS) image fusion method by using a deep neural network. In our method, a Siamese convolutional neural network (CNN) is applied to automatically generate a weight map which represents the saliency of each pixel for a pair of source images. A CNN plays a role in automatic encoding an image into a feature domain for classification. By applying the proposed method, the key problems in image fusion, which are the activity level measurement and fusion rule design, can be figured out in one shot. The fusion is carried out through the multi-scale image decomposition based on wavelet transform, and the reconstruction result is more perceptual to a human visual system. In addition, the visual qualitative effectiveness of the proposed fusion method is evaluated by comparing pedestrian detection results with other methods, by using the YOLOv3 object detector using a public benchmark dataset. The experimental results show that our proposed method showed competitive results in terms of both quantitative assessment and visual quality.

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He, Luxiao, Mi Wang, Ying Zhu, Xueli Chang, and Xiaoxiao Feng. "Image Fusion for High-Resolution Optical Satellites Based on Panchromatic Spectral Decomposition." Sensors 19, no. 11 (June 9, 2019): 2619. http://dx.doi.org/10.3390/s19112619.

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Ratio transformation methods are widely used for image fusion of high-resolution optical satellites. The premise for the use the ratio transformation is that there is a zero-bias linear relationship between the panchromatic band and the corresponding multi-spectral bands. However, there are bias terms and residual terms with large values in reality, depending on the sensors, the response spectral ranges, and the land-cover types. To address this problem, this paper proposes a panchromatic and multi-spectral image fusion method based on the panchromatic spectral decomposition (PSD). The low-resolution panchromatic and multi-spectral images are used to solve the proportionality coefficients, the bias coefficients, and the residual matrixes. These coefficients are substituted into the high-resolution panchromatic band and decompose it into the high-resolution multi-spectral bands. The experiments show that this method can make the fused image acquire high color fidelity and sharpness, it is robust to different sensors and features, and it can be applied to the panchromatic and multi-spectral fusion of high-resolution optical satellites.

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Yin, Ting, Wen Tao Zhang, and Xian Ming Xiong. "False Color Composite System of Multi-Spectral RS Images Based on IDL." Advanced Materials Research 468-471 (February 2012): 1671–74. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1671.

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Several methods of the choice in assembled spectral and color composite in the false color composite of multi-spectral RS images are discussed. The research indicates that using the OIF method to carry on the band choice, unifying the easy and feasible RGB false colored synthesis and carrying on the method of image linear extension can realize the false color composite of multi-spectral RS images effectively, and the obtaining false color image has rich information and distinctive visual effects.

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19

Chan, Li, Wan Xiaoxia, Xie Wei, Li Tianting, and Liang Jinxing. "Color filter design method for multi-channel spectral acquisition system." Journal of Applied Optics 37, no. 5 (2016): 495–502. http://dx.doi.org/10.5768/jao201637.0501001.

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20

Binyu Wang, 王彬宇, 徐海松 Haisong Xu, M. Ronnier Luo M. Ronnier Luo, and 郭晋一 Jinyi Guo. "Spectral-based color separation method for a multi-ink printer." Chinese Optics Letters 9, no. 6 (2011): 063301–63304. http://dx.doi.org/10.3788/col201109.063301.

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21

Raju, V. Bhagya, Dr K. Jaya Shankar, and Dr C. D. Naidu. "Highly Scalable Compression Method for Super Resolution Multi Spectral Images." IOSR journal of VLSI and Signal Processing 4, no. 3 (2014): 43–57. http://dx.doi.org/10.9790/4200-04314357.

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22

Forestier, M. Y., R. Pasquetti, R. Peyret, and C. Sabbah. "Spatial development of wakes using a spectral multi-domain method." Applied Numerical Mathematics 33, no. 1-4 (May 2000): 207–16. http://dx.doi.org/10.1016/s0168-9274(99)00085-9.

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23

Soleymani, Fazlollah. "Pricing multi-asset option problems: a Chebyshev pseudo-spectral method." BIT Numerical Mathematics 59, no. 1 (September 3, 2018): 243–70. http://dx.doi.org/10.1007/s10543-018-0722-0.

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24

Alharbi, Fahhad, and J. Campbell Scott. "Multi-domain spectral method for modal analysis of optical waveguide." Optical and Quantum Electronics 41, no. 8 (June 2009): 583–97. http://dx.doi.org/10.1007/s11082-010-9365-3.

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25

Sedaghat, S., S. Nemati, and Y. Ordokhani. "Convergence Analysis of Spectral Method for Neutral Multi-pantograph Equations." Iranian Journal of Science and Technology, Transactions A: Science 43, no. 5 (January 20, 2018): 2261–68. http://dx.doi.org/10.1007/s40995-017-0467-7.

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26

Chen, Feng, Qinwu Xu, and Jan S. Hesthaven. "A multi-domain spectral method for time-fractional differential equations." Journal of Computational Physics 293 (July 2015): 157–72. http://dx.doi.org/10.1016/j.jcp.2014.10.016.

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27

Tang, Siqin, Hong Li, and Baoli Yin. "A space-time spectral method for multi-dimensional Sobolev equations." Journal of Mathematical Analysis and Applications 499, no. 1 (July 2021): 124937. http://dx.doi.org/10.1016/j.jmaa.2021.124937.

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28

Zhang, Qin Lan, Zhi Yong Lu, and Xiang Liu. "Magnetic Spectral Analysis Method of Ferromagnetic Mixture." Applied Mechanics and Materials 602-605 (August 2014): 2721–25. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2721.

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There are different magnetic property of materials mixed in the mechanical wear of abrasive and powder processing. Depending on different alloy and magnetized material with different magnetic saturation characteristics, some substances reach the magnetic saturation in the magnetization processing soon, while some substances must be in a strong magnetic field to reach saturation, resulting in the magnetic property of combination material mixed with multi-component metal abrasive has multi-ingredient combination characteristics. Based on the PQ excitation measurement method, we analyze the magnetic property between the typical magnetic abrasive individual and combinations. We also build the magnetic spectral characteristics of the mixture which is made up of materials with different magnetic property, and finish the establishment of two mixtures mapping. This paper aims to explore the magnetic spectral analysis method of ferromagnetic mixture from the theory and experiment, and propose the magnetic spectrum analysis and calculation method of the mixture.

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29

CHAKRABORTY, DEBANANDA, JAE-HUN JUNG, and GAURAV KHANNA. "A MULTI-DOMAIN HYBRID METHOD FOR HEAD-ON COLLISION OF BLACK HOLES IN PARTICLE LIMIT." International Journal of Modern Physics C 22, no. 05 (May 2011): 517–41. http://dx.doi.org/10.1142/s0129183111016415.

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A hybrid method is developed based on the spectral and finite-difference methods for solving the inhomogeneous Zerilli equation in time-domain. The developed hybrid method decomposes the domain into the spectral and finite-difference domains. The singular source term is located in the spectral domain while the solution in the region without the singular term is approximated by the higher-order finite-difference method. The spectral domain is also split into multi-domains and the finite-difference domain is placed as the boundary domain. Due to the global nature of the spectral method, a multi-domain method composed of the spectral domain only does not yield the proper power-law decay unless the range of the computational domain is large. The finite-difference domain helps reduce boundary effects due to the truncation of the computational domain. The multi-domain approach with the finite-difference boundary domain method reduces the computational cost significantly and also yields the proper power-law decay. Stable and accurate interface conditions between the finite-difference and spectral domains and the spectral and spectral domains are derived. For the singular source term, we use both the Gaussian model with various values of full width at half-maximum and a localized discrete δ-function. The discrete δ-function was generalized to adopt the Gauss–Lobatto collocation points of the spectral domain. The gravitational waveforms are measured. Numerical results show that the developed hybrid method accurately yields the quasi-normal modes and the power-law decay profile. The numerical results also show that the power-law decay profile is less sensitive to the shape of the regularized δ-function for the Gaussian model than expected. The Gaussian model also yields better results than the localized discrete δ-function.

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30

Li, Dong, Lu, Lou, and Zhou. "Multi-Sensor Face Registration Based on Global and Local Structures." Applied Sciences 9, no. 21 (October 30, 2019): 4623. http://dx.doi.org/10.3390/app9214623.

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The work reported in this paper aims at utilizing the global geometrical relationship and local shape feature to register multi-spectral images for fusion-based face recognition. We first propose a multi-spectral face images registration method based on both global and local structures of feature point sets. In order to combine the global geometrical relationship and local shape feature in a new Student’s t Mixture probabilistic model framework. On the one hand, we use inner-distance shape context as the local shape descriptors of feature point sets. On the other hand, we formulate the feature point sets registration of the multi-spectral face images as the Student’s t Mixture probabilistic model estimation, and local shape descriptors are used to replace the mixing proportions of the prior Student’s t Mixture Model. Furthermore, in order to improve the anti-interference performance of face recognition techniques, a guided filtering and gradient preserving image fusion strategy is used to fuse the registered multi-spectral face image. It can make the multi-spectral fusion image hold more apparent details of the visible image and thermal radiation information of the infrared image. Subjective and objective registration experiments are conducted with manual selected landmarks and real multi-spectral face images. The qualitative and quantitative comparisons with the state-of-the-art methods demonstrate the accuracy and robustness of our proposed method in solving the multi-spectral face image registration problem.

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Huang, Hong, Zhengying Li, and Yinsong Pan. "Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification." Remote Sensing 11, no. 6 (March 17, 2019): 651. http://dx.doi.org/10.3390/rs11060651.

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Hyperspectral image (HSI) provides both spatial structure and spectral information for classification, but many traditional methods simply concatenate spatial features and spectral features together that usually lead to the curse-of-dimensionality and unbalanced representation of different features. To address this issue, a new dimensionality reduction (DR) method, termed multi-feature manifold discriminant analysis (MFMDA), was proposed in this paper. At first, MFMDA explores local binary patterns (LBP) operator to extract textural features for encoding the spatial information in HSI. Then, under graph embedding framework, the intrinsic and penalty graphs of LBP and spectral features are constructed to explore the discriminant manifold structure in both spatial and spectral domains, respectively. After that, a new spatial-spectral DR model for multi-feature fusion is built to extract discriminant spatial-spectral combined features, and it not only preserves the similarity relationship between spectral features and LBP features but also possesses strong discriminating ability in the low-dimensional embedding space. Experiments on Indian Pines, Heihe and Pavia University (PaviaU) hyperspectral data sets demonstrate that the proposed MFMDA method performs significantly better than some state-of-the-art methods using only single feature or simply stacking spectral features and spatial features together, and the classification accuracies of it can reach 95.43%, 97.19% and 96.60%, respectively.

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Liu, Qiang, Xiao Xia Wan, Zhen Liu, and Peng Sun. "Spectral Separation Method for Multi-Ink Printers Based on Color Constancy." Applied Mechanics and Materials 731 (January 2015): 18–21. http://dx.doi.org/10.4028/www.scientific.net/amm.731.18.

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Color constancy is a key metric for evaluating the color reproduction performance. This contribution proposed a color constancy based spectral separation method for muti-ink printers from the prospect of color perception. Basing on our previously developped spectral printer modeling workflow, a novel color constancy based spectral separation method for muti-ink printers was proposed, which achieved high-level color-constant color reproduction.The experiment results shows that the workflow described in the paper not only could makes full use of device gamut, but also improves the comprehensive color constancy performance obviously. Averagely speaking, the Color Inconstancy Index of reproduced colors is reduced from 2.884 △E00 to 2.016 △E00 , while maintaining reasonable spectral and colorimetric reproduction accuracy.

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33

Wang, Zifeng, Junguo Liu, Jinbao Li, and David Zhang. "Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2." Remote Sensing 10, no. 10 (October 16, 2018): 1643. http://dx.doi.org/10.3390/rs10101643.

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Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.

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Hu, Peng, Hongyuan Zhu, Xi Peng, and Jie Lin. "Semi-Supervised Multi-Modal Learning with Balanced Spectral Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 99–106. http://dx.doi.org/10.1609/aaai.v34i01.5339.

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Cross-modal retrieval aims to retrieve the relevant samples across different modalities, of which the key problem is how to model the correlations among different modalities while narrowing the large heterogeneous gap. In this paper, we propose a Semi-supervised Multimodal Learning Network method (SMLN) which correlates different modalities by capturing the intrinsic structure and discriminative correlation of the multimedia data. To be specific, the labeled and unlabeled data are used to construct a similarity matrix which integrates the cross-modal correlation, discrimination, and intra-modal graph information existing in the multimedia data. What is more important is that we propose a novel optimization approach to optimize our loss within a neural network which involves a spectral decomposition problem derived from a ratio trace criterion. Our optimization enjoys two advantages given below. On the one hand, the proposed approach is not limited to our loss, which could be applied to any case that is a neural network with the ratio trace criterion. On the other hand, the proposed optimization is different from existing ones which alternatively maximize the minor eigenvalues, thus overemphasizing the minor eigenvalues and ignore the dominant ones. In contrast, our method will exactly balance all eigenvalues, thus being more competitive to existing methods. Thanks to our loss and optimization strategy, our method could well preserve the discriminative and instinct information into the common space and embrace the scalability in handling large-scale multimedia data. To verify the effectiveness of the proposed method, extensive experiments are carried out on three widely-used multimodal datasets comparing with 13 state-of-the-art approaches.

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Mu, Guo, and Liu. "A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification." Remote Sensing 12, no. 1 (January 1, 2020): 125. http://dx.doi.org/10.3390/rs12010125.

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Extracting spatial and spectral features through deep neural networks has become an effective means of classification of hyperspectral images. However, most networks rarely consider the extraction of multi-scale spatial features and cannot fully integrate spatial and spectral features. In order to solve these problems, this paper proposes a multi-scale and multi-level spectral-spatial feature fusion network (MSSN) for hyperspectral image classification. The network uses the original 3D cube as input data and does not need to use feature engineering. In the MSSN, using different scale neighborhood blocks as the input of the network, the spectral-spatial features of different scales can be effectively extracted. The proposed 3D–2D alternating residual block combines the spectral features extracted by the three-dimensional convolutional neural network (3D-CNN) with the spatial features extracted by the two-dimensional convolutional neural network (2D-CNN). It not only achieves the fusion of spectral features and spatial features but also achieves the fusion of high-level features and low-level features. Experimental results on four hyperspectral datasets show that this method is superior to several state-of-the-art classification methods for hyperspectral images.

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Yang, In-Hyung, Jae-Eun Jeong, Hyung-Taek Kwak, and Jae-Eung Oh. "Noise Reduction of PDP TV Using Multi-dimensional Spectral Analysis Method." Transactions of the Korean Society for Noise and Vibration Engineering 21, no. 1 (January 20, 2011): 81–88. http://dx.doi.org/10.5050/ksnve.2011.21.1.081.

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ZHANG Yan-chao, 张艳超, 孙强 SUN Qiang, 赵建 ZHAO Jian, 李也凡 LI Ye-fan, 韩希珍 HAN Xi-zhen, and 白晶 BAI Jing. "Auto-focusing adjustment of multi-spectral imager by differential projection method." Optics and Precision Engineering 21, no. 8 (2013): 2023–30. http://dx.doi.org/10.3788/ope.20132108.2023.

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Feilhauer, Hannes, Gregory P. Asner, and Roberta E. Martin. "Multi-method ensemble selection of spectral bands related to leaf biochemistry." Remote Sensing of Environment 164 (July 2015): 57–65. http://dx.doi.org/10.1016/j.rse.2015.03.033.

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Ishii, Jun-ichiro. "Color conversion method for multi-primary display for spectral color reproduction." Journal of Electronic Imaging 13, no. 4 (October 1, 2004): 701. http://dx.doi.org/10.1117/1.1785800.

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Tibaldi, Alberto, Giuseppe Addamo, Oscar Antonio Peverini, Renato Orta, Giuseppe Virone, and Riccardo Tascone. "Analysis of Axisymmetric Waveguide Components by a Multi-Domain Spectral Method." IEEE Transactions on Microwave Theory and Techniques 63, no. 1 (January 2015): 115–24. http://dx.doi.org/10.1109/tmtt.2014.2376561.

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Jeyaseelan, A. S., and R. Balaji. "Spectral analysis of wave elevation time histories using multi-taper method." Ocean Engineering 105 (September 2015): 242–46. http://dx.doi.org/10.1016/j.oceaneng.2015.06.051.

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Jung, Andr ás, Christian Götze, and Cornelia Glässer. "White-reference based post-correction method for multi-source spectral libraries." Photogrammetrie - Fernerkundung - Geoinformation 2010, no. 5 (November 1, 2010): 363–69. http://dx.doi.org/10.1127/1432-8364/2010/0062.

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Cao, Jingya, Wenze Xia, Shaokun Han, Liang Wang, and Yu Zhai. "Fiber array coupling based multi-spectral streak tube detection imaging method." Review of Scientific Instruments 89, no. 7 (July 2018): 073106. http://dx.doi.org/10.1063/1.5001251.

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Ouafek, Naouel, and Mohamed Khireddine Kholladi. "A restoration and binarization method for multi-spectral damaged document image." International Journal of Signal and Imaging Systems Engineering 11, no. 3 (2018): 182. http://dx.doi.org/10.1504/ijsise.2018.093283.

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Ouafek, Naouel, and Mohamed Khireddine Kholladi. "A restoration and binarisation method for multi-spectral damaged document image." International Journal of Signal and Imaging Systems Engineering 11, no. 3 (2018): 182. http://dx.doi.org/10.1504/ijsise.2018.10014299.

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Alharbi, Fahhad. "Predefined Exponential Basis Set for Half-Bounded Multi Domain Spectral Method." Applied Mathematics 01, no. 03 (2010): 146–52. http://dx.doi.org/10.4236/am.2010.13019.

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Pan, Tao, Weiqun Xu, Hongping Shen, and Dingzhou Xu. "Spectral Correction Method of Multi-Channels Near-Infrared Spectrometer and Applications." American Journal of Analytical Chemistry 08, no. 02 (2017): 158–70. http://dx.doi.org/10.4236/ajac.2017.82013.

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Zhang, Cheng Wen, Jia Kui Tang, Su Juan Mi, Li Jun Zhao, Yong Zhi Li, and Xin Ju Yu. "A New Data Transformation Method for Cbers-02b Multi-Spectral Images." Key Engineering Materials 500 (January 2012): 444–49. http://dx.doi.org/10.4028/www.scientific.net/kem.500.444.

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Abstract:

According to the detailed studies on the data of China-Brazil earth resources satellite-02B (CBERS-02B) and the LBV data transform method proposed by the previous researcher, a new LBV data transformation equations for CBERS-02B data was specially proposed. A transformation experiment on CBERS-02B data showed that the LBV transformed result images were more vivid, and the features of result images were more easily to be classified than the fault color composite images of original bands. In order to evaluate the performance of this proposed method, the maximum likelihood supervised classification method was used. The final classification results showed that the accuracy of the LBV transformed image is obviously better than that of the fault color composite images of original bands, which demonstrated that the proposed LBV transformation method for CBERS-02B has good potential for CBERS-02B data in the future applications.

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Meng Hongjuan, 孟红娟, 陈平 Chen Ping, 潘晋孝 Pan Jinxiao, and 李毅红 Li Yihong. "X-Ray Multi-Spectral CT Imaging Method Based on Subtraction Fusion." Laser & Optoelectronics Progress 57, no. 8 (2020): 083001. http://dx.doi.org/10.3788/lop57.083001.

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Song, Wenbin, Long Wen, Liang Gao, and Xinyu Li. "Unsupervised fault diagnosis method based on iterative multi‐manifold spectral clustering." IET Collaborative Intelligent Manufacturing 1, no. 2 (June 2019): 48–55. http://dx.doi.org/10.1049/iet-cim.2019.0003.

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