Noise reduction in the spectral domain of hyperspectral images using denoising autoencoder methods
Denoising of spectra has been a great challenge in hyperspectral image analysis. Near-infrared hyperspectral images of milk powder, rice flour and soybean flour were acquired and denoising in the spectral domain were studied. Noise free spectra and noises were simulated based on sample pixel-wise sp...
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| Veröffentlicht in: | Chemometrics and intelligent laboratory systems Jg. 203; S. 104063 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
15.08.2020
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| ISSN: | 0169-7439, 1873-3239 |
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| Abstract | Denoising of spectra has been a great challenge in hyperspectral image analysis. Near-infrared hyperspectral images of milk powder, rice flour and soybean flour were acquired and denoising in the spectral domain were studied. Noise free spectra and noises were simulated based on sample pixel-wise spectra. The noisy spectra with signal to noise ratio (SNR) around 45 dB (similar to real pixel-wise spectra) were simulated. The simulated noisy spectra were preprocessed by traditional methods as moving average smoothing (MAS), Savitzky-Golay smoothing (SGS), wavelet transform (WT) and empirical mode decomposition (EMD). The basic denoising autoencoder (DAE-1) and the stacked DAE (DAE-2) were studied for denoising. The noisy spectra with SNR around 35 dB and 55 dB were further simulated to explore the effectiveness of DAE based methods. DAE-1 and DAE-2 performed better than the other methods, with higher SNR, lower mean squared error (MSE) and mean absolute error (MAE). The developed DAE methods were applied to real-world pixel-wise spectra with good performances. The overall results proved the feasibility of using DAE based methods for noise reduction in the spectral domain of hyperspectral images, and the DAE based methods have great potential to be extended to spectral denoising of other vibrational spectroscopy techniques.
•Denoising autoencoder based methods were used for pixel-wise spectra denoising.•Noise-free spectra and noises were simulated by real-world pixel-wise spectra.•Four different conventional spectral denoising methods were used for comparison.•Real-world pixel-wise spectra denoising was conducted using the developed methods. |
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| AbstractList | Denoising of spectra has been a great challenge in hyperspectral image analysis. Near-infrared hyperspectral images of milk powder, rice flour and soybean flour were acquired and denoising in the spectral domain were studied. Noise free spectra and noises were simulated based on sample pixel-wise spectra. The noisy spectra with signal to noise ratio (SNR) around 45 dB (similar to real pixel-wise spectra) were simulated. The simulated noisy spectra were preprocessed by traditional methods as moving average smoothing (MAS), Savitzky-Golay smoothing (SGS), wavelet transform (WT) and empirical mode decomposition (EMD). The basic denoising autoencoder (DAE-1) and the stacked DAE (DAE-2) were studied for denoising. The noisy spectra with SNR around 35 dB and 55 dB were further simulated to explore the effectiveness of DAE based methods. DAE-1 and DAE-2 performed better than the other methods, with higher SNR, lower mean squared error (MSE) and mean absolute error (MAE). The developed DAE methods were applied to real-world pixel-wise spectra with good performances. The overall results proved the feasibility of using DAE based methods for noise reduction in the spectral domain of hyperspectral images, and the DAE based methods have great potential to be extended to spectral denoising of other vibrational spectroscopy techniques.
•Denoising autoencoder based methods were used for pixel-wise spectra denoising.•Noise-free spectra and noises were simulated by real-world pixel-wise spectra.•Four different conventional spectral denoising methods were used for comparison.•Real-world pixel-wise spectra denoising was conducted using the developed methods. |
| ArticleNumber | 104063 |
| Author | Liu, Fei He, Yong Zhang, Chu Zhu, Susu Zhao, Yiying Zhou, Lei |
| Author_xml | – sequence: 1 givenname: Chu surname: Zhang fullname: Zhang, Chu organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China – sequence: 2 givenname: Lei surname: Zhou fullname: Zhou, Lei organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China – sequence: 3 givenname: Yiying surname: Zhao fullname: Zhao, Yiying organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China – sequence: 4 givenname: Susu surname: Zhu fullname: Zhu, Susu organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China – sequence: 5 givenname: Fei surname: Liu fullname: Liu, Fei organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China – sequence: 6 givenname: Yong surname: He fullname: He, Yong email: yhe@zju.edu.cn organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China |
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| Cites_doi | 10.1080/05704928.2013.838678 10.1016/j.jpba.2014.04.012 10.1016/j.postharvbio.2013.04.017 10.1049/htl.2015.0007 10.1111/1541-4337.12492 10.3390/app8020212 10.1016/j.ymssp.2017.11.024 10.1080/05704928.2012.705800 10.1016/j.jfoodeng.2016.07.015 10.3390/mti2030047 10.1186/s13007-019-0476-y 10.1109/LGRS.2017.2743738 10.1016/j.foodchem.2016.04.044 10.3390/s19194065 10.1109/ACCESS.2019.2917267 10.1039/C7AY02115A 10.1016/j.biosystemseng.2017.04.006 10.1109/TGRS.2018.2865197 10.1146/annurev-food-032818-121155 10.3390/s131013289 10.1016/j.biosystemseng.2007.11.007 10.1186/s13007-015-0072-8 10.1016/j.jfoodeng.2013.06.005 10.1021/acs.analchem.6b02969 10.1186/s13007-017-0233-z 10.1021/ci980210j 10.1093/biomet/81.3.425 10.1016/j.trac.2018.08.013 10.1016/j.infrared.2015.12.008 10.1016/j.aca.2016.12.010 10.1038/s41598-018-20270-y 10.1063/1.4822961 10.1021/ac960638m |
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| Keywords | Denoising autoencoder Empirical mode decomposition Wavelet transform Hyperspectral image Spectra simulation Noise reduction |
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| References | Rungpichayapichet, Nagle, Yuwanbun, Khuwijitjaru, Mahayothee, Mueller (bib11) 2017; 159 Khan, Yairi (bib21) 2018; 107 Yuan, Zhang, Li, Shen, Zhang (bib28) 2019; 57 Lu, Tsao, Matsuda, Hori (bib31) 2013 Donoho, Johnstone (bib36) 1994; 81 Lowe, Harrison, French (bib4) 2017; 13 Humplik, Lazar, Husickova, Spichal (bib5) 2015; 11 Bansal, Sharma, Singh (bib22) 2019; 559 Qiu, Chen, Zhao, Zhu, He, Zhang (bib25) 2018; 8 Lu, Fei (bib6) 2014; 19 Sacre, De Bleye, Chavez, Netchacovitch, Hubert, Ziemons (bib9) 2014; 101 Ma, Sun, Pu, Cheng, Wei (bib1) 2019; 10 Ishii, Komiyama, Shinozaki, Horiuchi, Kuroiwa (bib30) 2013 Ambrose, Kandpal, Kim, Lee, Cho (bib12) 2016; 75 Zhou, Zhang, Liu, Qiu, He (bib24) 2019; 18 Zhang, Liu, He (bib17) 2018; 8 Williams, Kucheryavskiy (bib20) 2016; 209 Lahmiri (bib38) 2015; 3 Bakator, Radosav (bib23) 2018; 2 Vincent, Larochelle, Bengio, Manzagol (bib29) 2008 Press, Teukolsky (bib32) 1990; 4 Calin, Parasca, Savastru, Manea (bib7) 2014; 49 Kandpal, Lee, Kim, Mo, Cho (bib10) 2013; 13 Cai, Harrington (bib34) 1998; 38 Li, He (bib35) 2008; 99 Feng, Zhu, Zhou, Zhao, Bao, Zhang (bib16) 2019; 7 Diezma, Lleo, Roger, Herrero-Langreo, Lunadei, Ruiz-Altisent (bib19) 2013; 85 Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov (bib37) 2014; 15 Dale, Thewis, Boudry, Rotar, Dardenne, Baeten (bib2) 2013; 48 Zhu, Zhou, Zhang, Bao, Wu, Chu (bib39) 2019; 19 Cheng, Jin, Xu, Zheng (bib13) 2017; 9 Xie, Li (bib27) 2017; 14 Acquarelli, van Laarhoven, Gerretzen, Tran, Buydens, Marchiori (bib26) 2017; 954 Yang, Sun, Cheng (bib14) 2017; 192 Mazivila, Olivieri (bib8) 2018; 108 Lara, Lleo, Diezma-Iglesias, Roger, Ruiz-Altisent (bib18) 2013; 119 Kandpal, Tewari, Gopinathan, Boulas, Cho (bib15) 2016; 88 Barclay, Bonner, Hamilton (bib33) 1997; 69 Feng, Zhu, Liu, He, Bao, Zhang (bib3) 2019; 15 Donoho (10.1016/j.chemolab.2020.104063_bib36) 1994; 81 Mazivila (10.1016/j.chemolab.2020.104063_bib8) 2018; 108 Li (10.1016/j.chemolab.2020.104063_bib35) 2008; 99 Bansal (10.1016/j.chemolab.2020.104063_bib22) 2019; 559 Humplik (10.1016/j.chemolab.2020.104063_bib5) 2015; 11 Diezma (10.1016/j.chemolab.2020.104063_bib19) 2013; 85 Cheng (10.1016/j.chemolab.2020.104063_bib13) 2017; 9 Kandpal (10.1016/j.chemolab.2020.104063_bib15) 2016; 88 Feng (10.1016/j.chemolab.2020.104063_bib3) 2019; 15 Xie (10.1016/j.chemolab.2020.104063_bib27) 2017; 14 Bakator (10.1016/j.chemolab.2020.104063_bib23) 2018; 2 Lara (10.1016/j.chemolab.2020.104063_bib18) 2013; 119 Kandpal (10.1016/j.chemolab.2020.104063_bib10) 2013; 13 Ambrose (10.1016/j.chemolab.2020.104063_bib12) 2016; 75 Khan (10.1016/j.chemolab.2020.104063_bib21) 2018; 107 Zhu (10.1016/j.chemolab.2020.104063_bib39) 2019; 19 Lowe (10.1016/j.chemolab.2020.104063_bib4) 2017; 13 Sacre (10.1016/j.chemolab.2020.104063_bib9) 2014; 101 Qiu (10.1016/j.chemolab.2020.104063_bib25) 2018; 8 Cai (10.1016/j.chemolab.2020.104063_bib34) 1998; 38 Ma (10.1016/j.chemolab.2020.104063_bib1) 2019; 10 Press (10.1016/j.chemolab.2020.104063_bib32) 1990; 4 Yuan (10.1016/j.chemolab.2020.104063_bib28) 2019; 57 Yang (10.1016/j.chemolab.2020.104063_bib14) 2017; 192 Vincent (10.1016/j.chemolab.2020.104063_bib29) 2008 Calin (10.1016/j.chemolab.2020.104063_bib7) 2014; 49 Ishii (10.1016/j.chemolab.2020.104063_bib30) 2013 Williams (10.1016/j.chemolab.2020.104063_bib20) 2016; 209 Lahmiri (10.1016/j.chemolab.2020.104063_bib38) 2015; 3 Zhou (10.1016/j.chemolab.2020.104063_bib24) 2019; 18 Feng (10.1016/j.chemolab.2020.104063_bib16) 2019; 7 Acquarelli (10.1016/j.chemolab.2020.104063_bib26) 2017; 954 Barclay (10.1016/j.chemolab.2020.104063_bib33) 1997; 69 Dale (10.1016/j.chemolab.2020.104063_bib2) 2013; 48 Srivastava (10.1016/j.chemolab.2020.104063_bib37) 2014; 15 Rungpichayapichet (10.1016/j.chemolab.2020.104063_bib11) 2017; 159 Zhang (10.1016/j.chemolab.2020.104063_bib17) 2018; 8 Lu (10.1016/j.chemolab.2020.104063_bib31) 2013 Lu (10.1016/j.chemolab.2020.104063_bib6) 2014; 19 |
| References_xml | – start-page: 3479 year: 2013 end-page: 3483 ident: bib30 article-title: Reverberant Speech Recognition Based on Denoising Autoencoder, 14th Annual Conference of the International Speech Communication Association – volume: 49 start-page: 435 year: 2014 end-page: 447 ident: bib7 article-title: Hyperspectral imaging in the medical field: present and future publication-title: Appl. Spectrosc. Rev. – volume: 48 start-page: 142 year: 2013 end-page: 159 ident: bib2 article-title: Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review publication-title: Appl. Spectrosc. Rev. – volume: 13 start-page: 13289 year: 2013 end-page: 13300 ident: bib10 article-title: Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast publication-title: Sensors – volume: 11 start-page: 29 year: 2015 ident: bib5 article-title: Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses-a review publication-title: Plant Methods – start-page: 1096 year: 2008 end-page: 1103 ident: bib29 article-title: Extracting and composing robust features with denoising autoencoders publication-title: Proceedings of the 25th international conference on Machine learning – volume: 8 start-page: 2166 year: 2018 ident: bib17 article-title: Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis publication-title: Sci. Rep-UK. – volume: 954 start-page: 22 year: 2017 end-page: 31 ident: bib26 article-title: Convolutional neural networks for vibrational spectroscopic data analysis publication-title: Anal. Chim. Acta – volume: 2 start-page: 47 year: 2018 ident: bib23 article-title: Deep learning and medical diagnosis: a review of literature publication-title: Multimodal Technologies Interact – volume: 192 start-page: 53 year: 2017 end-page: 60 ident: bib14 article-title: Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process publication-title: J. Food Eng. – volume: 18 start-page: 1793 year: 2019 end-page: 1811 ident: bib24 article-title: Application of deep learning in food: a review publication-title: Compr. Rev. Food Sci. F. – volume: 4 start-page: 869 year: 1990 end-page: 872 ident: bib32 article-title: Savitzky-golay smoothing filters publication-title: Comput. Phys. – volume: 19 start-page: 4065 year: 2019 ident: bib39 article-title: Identification of soybean varieties using hyperspectral imaging coupled with convolutional neural network publication-title: Sensors – volume: 69 start-page: 78 year: 1997 end-page: 90 ident: bib33 article-title: Application of wavelet transforms to experimental spectra: smoothing, denoising, and data set compression publication-title: Anal. Chem. – volume: 57 start-page: 1205 year: 2019 end-page: 1218 ident: bib28 article-title: Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network publication-title: IEEE T. Geosci. Remote – volume: 81 start-page: 425 year: 1994 end-page: 455 ident: bib36 article-title: Ideal spatial adaptation by wavelet shrinkage publication-title: Biometrika – start-page: 436 year: 2013 end-page: 440 ident: bib31 article-title: Speech enhancement based on deep denoising autoencoder, 14th annual conference of the international speech communication association – volume: 15 start-page: 1929 year: 2014 end-page: 1958 ident: bib37 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 64494 year: 2019 end-page: 64505 ident: bib16 article-title: Detection of subtle bruises on winter jujube using hyperspectral imaging with pixel-wise deep learning method publication-title: IEEE Access – volume: 85 start-page: 8 year: 2013 end-page: 17 ident: bib19 article-title: Examination of the quality of spinach leaves using hyperspectral imaging publication-title: Postharvest Biol. Technol. – volume: 75 start-page: 173 year: 2016 end-page: 179 ident: bib12 article-title: High speed measurement of corn seed viability using hyperspectral imaging publication-title: Infrared Phys. Technol. – volume: 38 start-page: 1161 year: 1998 end-page: 1170 ident: bib34 article-title: Different discrete wavelet transforms applied to denoising analytical data publication-title: J. Chem. Inf. Comput. Sci. – volume: 15 start-page: 91 year: 2019 ident: bib3 article-title: Hyperspectral imaging for seed quality and safety inspection: a review publication-title: Plant Methods – volume: 13 start-page: 80 year: 2017 ident: bib4 article-title: Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress publication-title: Plant Methods – volume: 9 start-page: 6148 year: 2017 end-page: 6154 ident: bib13 article-title: NIR hyperspectral imaging with multivariate analysis for measurement of oil and protein contents in peanut varieties publication-title: Anal. Methods-UK – volume: 3 start-page: 67 year: 2015 end-page: 71 ident: bib38 article-title: Image denoising in bidimensional empirical mode decomposition domain: the role of Student’s probability distribution function publication-title: Healthc. Technol. Lett. – volume: 119 start-page: 353 year: 2013 end-page: 361 ident: bib18 article-title: Monitoring spinach shelf-life with hyperspectral image through packaging films publication-title: J. Food Eng. – volume: 559 start-page: 374 year: 2019 end-page: 381 ident: bib22 article-title: A review on the application of deep learning in legal domain publication-title: IFIP International Conference on Artificial Intelligence Applications and Innovations – volume: 88 start-page: 11055 year: 2016 end-page: 11061 ident: bib15 article-title: In-process control assay of pharmaceutical microtablets using hyperspectral imaging coupled with multivariate analysis publication-title: Anal. Chem. – volume: 209 start-page: 131 year: 2016 end-page: 138 ident: bib20 article-title: Classification of maize kernels using NIR hyperspectral imaging publication-title: Food Chem. – volume: 19 year: 2014 ident: bib6 article-title: Medical hyperspectral imaging: a review publication-title: J. Biomed. Optic. – volume: 14 start-page: 1963 year: 2017 end-page: 1967 ident: bib27 article-title: Hyperspectral imagery denoising by deep learning with trainable nonlinearity function publication-title: IEEE T. Geosci. Remote – volume: 108 start-page: 74 year: 2018 end-page: 87 ident: bib8 article-title: Chemometrics coupled to vibrational spectroscopy and spectroscopic imaging for the analysis of solid-phase pharmaceutical products: a brief review on non-destructive analytical methods publication-title: Trac. Trends Anal. Chem. – volume: 99 start-page: 313 year: 2008 end-page: 321 ident: bib35 article-title: Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks publication-title: Biosyst. Eng. – volume: 10 start-page: 197 year: 2019 end-page: 220 ident: bib1 article-title: Advanced techniques for hyperspectral imaging in the food industry: principles and recent applications publication-title: Annu. Rev. Food Sci. T. – volume: 159 start-page: 109 year: 2017 end-page: 120 ident: bib11 article-title: Prediction mapping of physicochemical properties in mango by hyperspectral imaging publication-title: Biosyst. Eng. – volume: 8 start-page: 212 year: 2018 ident: bib25 article-title: Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network publication-title: Appl. Sci.-Basel. – volume: 107 start-page: 241 year: 2018 end-page: 265 ident: bib21 article-title: A review on the application of deep learning in system health management publication-title: Mech. Syst. Signal Process. – volume: 101 start-page: 123 year: 2014 end-page: 140 ident: bib9 article-title: Data processing of vibrational chemical imaging for pharmaceutical applications publication-title: J. Pharmaceut. Biomed. – volume: 49 start-page: 435 year: 2014 ident: 10.1016/j.chemolab.2020.104063_bib7 article-title: Hyperspectral imaging in the medical field: present and future publication-title: Appl. Spectrosc. Rev. doi: 10.1080/05704928.2013.838678 – volume: 101 start-page: 123 year: 2014 ident: 10.1016/j.chemolab.2020.104063_bib9 article-title: Data processing of vibrational chemical imaging for pharmaceutical applications publication-title: J. Pharmaceut. Biomed. doi: 10.1016/j.jpba.2014.04.012 – volume: 85 start-page: 8 year: 2013 ident: 10.1016/j.chemolab.2020.104063_bib19 article-title: Examination of the quality of spinach leaves using hyperspectral imaging publication-title: Postharvest Biol. Technol. doi: 10.1016/j.postharvbio.2013.04.017 – volume: 3 start-page: 67 year: 2015 ident: 10.1016/j.chemolab.2020.104063_bib38 article-title: Image denoising in bidimensional empirical mode decomposition domain: the role of Student’s probability distribution function publication-title: Healthc. Technol. Lett. doi: 10.1049/htl.2015.0007 – volume: 19 year: 2014 ident: 10.1016/j.chemolab.2020.104063_bib6 article-title: Medical hyperspectral imaging: a review publication-title: J. Biomed. Optic. – volume: 18 start-page: 1793 year: 2019 ident: 10.1016/j.chemolab.2020.104063_bib24 article-title: Application of deep learning in food: a review publication-title: Compr. Rev. Food Sci. F. doi: 10.1111/1541-4337.12492 – volume: 8 start-page: 212 year: 2018 ident: 10.1016/j.chemolab.2020.104063_bib25 article-title: Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network publication-title: Appl. Sci.-Basel. doi: 10.3390/app8020212 – volume: 107 start-page: 241 year: 2018 ident: 10.1016/j.chemolab.2020.104063_bib21 article-title: A review on the application of deep learning in system health management publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2017.11.024 – volume: 48 start-page: 142 year: 2013 ident: 10.1016/j.chemolab.2020.104063_bib2 article-title: Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review publication-title: Appl. Spectrosc. Rev. doi: 10.1080/05704928.2012.705800 – volume: 192 start-page: 53 year: 2017 ident: 10.1016/j.chemolab.2020.104063_bib14 article-title: Development of simplified models for nondestructive hyperspectral imaging monitoring of TVB-N contents in cured meat during drying process publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2016.07.015 – start-page: 436 year: 2013 ident: 10.1016/j.chemolab.2020.104063_bib31 – volume: 2 start-page: 47 year: 2018 ident: 10.1016/j.chemolab.2020.104063_bib23 article-title: Deep learning and medical diagnosis: a review of literature publication-title: Multimodal Technologies Interact doi: 10.3390/mti2030047 – volume: 15 start-page: 91 year: 2019 ident: 10.1016/j.chemolab.2020.104063_bib3 article-title: Hyperspectral imaging for seed quality and safety inspection: a review publication-title: Plant Methods doi: 10.1186/s13007-019-0476-y – volume: 14 start-page: 1963 year: 2017 ident: 10.1016/j.chemolab.2020.104063_bib27 article-title: Hyperspectral imagery denoising by deep learning with trainable nonlinearity function publication-title: IEEE T. Geosci. Remote doi: 10.1109/LGRS.2017.2743738 – volume: 15 start-page: 1929 year: 2014 ident: 10.1016/j.chemolab.2020.104063_bib37 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J. Mach. Learn. Res. – volume: 209 start-page: 131 year: 2016 ident: 10.1016/j.chemolab.2020.104063_bib20 article-title: Classification of maize kernels using NIR hyperspectral imaging publication-title: Food Chem. doi: 10.1016/j.foodchem.2016.04.044 – volume: 19 start-page: 4065 year: 2019 ident: 10.1016/j.chemolab.2020.104063_bib39 article-title: Identification of soybean varieties using hyperspectral imaging coupled with convolutional neural network publication-title: Sensors doi: 10.3390/s19194065 – volume: 559 start-page: 374 year: 2019 ident: 10.1016/j.chemolab.2020.104063_bib22 article-title: A review on the application of deep learning in legal domain publication-title: IFIP International Conference on Artificial Intelligence Applications and Innovations – start-page: 3479 year: 2013 ident: 10.1016/j.chemolab.2020.104063_bib30 – volume: 7 start-page: 64494 year: 2019 ident: 10.1016/j.chemolab.2020.104063_bib16 article-title: Detection of subtle bruises on winter jujube using hyperspectral imaging with pixel-wise deep learning method publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917267 – volume: 9 start-page: 6148 year: 2017 ident: 10.1016/j.chemolab.2020.104063_bib13 article-title: NIR hyperspectral imaging with multivariate analysis for measurement of oil and protein contents in peanut varieties publication-title: Anal. Methods-UK doi: 10.1039/C7AY02115A – volume: 159 start-page: 109 year: 2017 ident: 10.1016/j.chemolab.2020.104063_bib11 article-title: Prediction mapping of physicochemical properties in mango by hyperspectral imaging publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2017.04.006 – volume: 57 start-page: 1205 year: 2019 ident: 10.1016/j.chemolab.2020.104063_bib28 article-title: Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network publication-title: IEEE T. Geosci. Remote doi: 10.1109/TGRS.2018.2865197 – start-page: 1096 year: 2008 ident: 10.1016/j.chemolab.2020.104063_bib29 article-title: Extracting and composing robust features with denoising autoencoders publication-title: Proceedings of the 25th international conference on Machine learning – volume: 10 start-page: 197 year: 2019 ident: 10.1016/j.chemolab.2020.104063_bib1 article-title: Advanced techniques for hyperspectral imaging in the food industry: principles and recent applications publication-title: Annu. Rev. Food Sci. T. doi: 10.1146/annurev-food-032818-121155 – volume: 13 start-page: 13289 year: 2013 ident: 10.1016/j.chemolab.2020.104063_bib10 article-title: Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast publication-title: Sensors doi: 10.3390/s131013289 – volume: 99 start-page: 313 year: 2008 ident: 10.1016/j.chemolab.2020.104063_bib35 article-title: Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks publication-title: Biosyst. Eng. doi: 10.1016/j.biosystemseng.2007.11.007 – volume: 11 start-page: 29 year: 2015 ident: 10.1016/j.chemolab.2020.104063_bib5 article-title: Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses-a review publication-title: Plant Methods doi: 10.1186/s13007-015-0072-8 – volume: 119 start-page: 353 year: 2013 ident: 10.1016/j.chemolab.2020.104063_bib18 article-title: Monitoring spinach shelf-life with hyperspectral image through packaging films publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2013.06.005 – volume: 88 start-page: 11055 year: 2016 ident: 10.1016/j.chemolab.2020.104063_bib15 article-title: In-process control assay of pharmaceutical microtablets using hyperspectral imaging coupled with multivariate analysis publication-title: Anal. Chem. doi: 10.1021/acs.analchem.6b02969 – volume: 13 start-page: 80 year: 2017 ident: 10.1016/j.chemolab.2020.104063_bib4 article-title: Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress publication-title: Plant Methods doi: 10.1186/s13007-017-0233-z – volume: 38 start-page: 1161 year: 1998 ident: 10.1016/j.chemolab.2020.104063_bib34 article-title: Different discrete wavelet transforms applied to denoising analytical data publication-title: J. Chem. Inf. Comput. Sci. doi: 10.1021/ci980210j – volume: 81 start-page: 425 year: 1994 ident: 10.1016/j.chemolab.2020.104063_bib36 article-title: Ideal spatial adaptation by wavelet shrinkage publication-title: Biometrika doi: 10.1093/biomet/81.3.425 – volume: 108 start-page: 74 year: 2018 ident: 10.1016/j.chemolab.2020.104063_bib8 article-title: Chemometrics coupled to vibrational spectroscopy and spectroscopic imaging for the analysis of solid-phase pharmaceutical products: a brief review on non-destructive analytical methods publication-title: Trac. Trends Anal. Chem. doi: 10.1016/j.trac.2018.08.013 – volume: 75 start-page: 173 year: 2016 ident: 10.1016/j.chemolab.2020.104063_bib12 article-title: High speed measurement of corn seed viability using hyperspectral imaging publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2015.12.008 – volume: 954 start-page: 22 year: 2017 ident: 10.1016/j.chemolab.2020.104063_bib26 article-title: Convolutional neural networks for vibrational spectroscopic data analysis publication-title: Anal. Chim. Acta doi: 10.1016/j.aca.2016.12.010 – volume: 8 start-page: 2166 year: 2018 ident: 10.1016/j.chemolab.2020.104063_bib17 article-title: Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis publication-title: Sci. Rep-UK. doi: 10.1038/s41598-018-20270-y – volume: 4 start-page: 869 year: 1990 ident: 10.1016/j.chemolab.2020.104063_bib32 article-title: Savitzky-golay smoothing filters publication-title: Comput. Phys. doi: 10.1063/1.4822961 – volume: 69 start-page: 78 year: 1997 ident: 10.1016/j.chemolab.2020.104063_bib33 article-title: Application of wavelet transforms to experimental spectra: smoothing, denoising, and data set compression publication-title: Anal. Chem. doi: 10.1021/ac960638m |
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