Academic literature on the topic 'Hyperspectral imaging'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Hyperspectral imaging.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Hyperspectral imaging"
V, Prathama, and Dr Thippeswamy G. "Food Safety Control Using Hyperspectral Imaging." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 796–806. http://dx.doi.org/10.31142/ijtsrd10983.
Full textMüller-Rowold, M., and R. Reulke. "HYPERSPECTRAL PANORAMIC IMAGING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 323–28. http://dx.doi.org/10.5194/isprs-archives-xlii-1-323-2018.
Full textBhargava, Rohit, and Kianoush Falahkheirkhah. "Enhancing hyperspectral imaging." Nature Machine Intelligence 3, no. 4 (April 2021): 279–80. http://dx.doi.org/10.1038/s42256-021-00336-9.
Full textRui Zhou, Rui Zhou, Manping Ye Manping Ye, and Huacai Chen Huacai Chen. "Apple bruise detect with hyperspectral imaging technique." Chinese Optics Letters 12, s1 (2014): S11101–311103. http://dx.doi.org/10.3788/col201412.s11101.
Full textChangsheng Liu, Changsheng Liu, Zhimin Han Zhimin Han, and Tianyu Xie Tianyu Xie. "Hyperspectral high-dynamic-range endoscopic mucosal imaging." Chinese Optics Letters 13, no. 7 (2015): 071701–71705. http://dx.doi.org/10.3788/col201513.071701.
Full textLu, Bing, Phuong D. Dao, Jiangui Liu, Yuhong He, and Jiali Shang. "Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture." Remote Sensing 12, no. 16 (August 18, 2020): 2659. http://dx.doi.org/10.3390/rs12162659.
Full textWang, Zhixin, Peng Xu, Bohan Liu, Yankun Cao, Zhi Liu, and Zhaojun Liu. "Hyperspectral imaging for underwater object detection." Sensor Review 41, no. 2 (April 5, 2021): 176–91. http://dx.doi.org/10.1108/sr-07-2020-0165.
Full textChang, Chein-I., Meiping Song, Junping Zhang, and Chao-Cheng Wu. "Editorial for Special Issue “Hyperspectral Imaging and Applications”." Remote Sensing 11, no. 17 (August 27, 2019): 2012. http://dx.doi.org/10.3390/rs11172012.
Full textZou, Chunbo, Jianfeng Yang, Dengshan Wu, Qiang Zhao, Yuquan Gan, Di Fu, Fanchao Yang, Hong Liu, Qinglan Bai, and Bingliang Hu. "Design and Test of Portable Hyperspectral Imaging Spectrometer." Journal of Sensors 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7692491.
Full textStuart, Mary B., Andrew J. S. McGonigle, Matthew Davies, Matthew J. Hobbs, Nicholas A. Boone, Leigh R. Stanger, Chengxi Zhu, Tom D. Pering, and Jon R. Willmott. "Low-Cost Hyperspectral Imaging with A Smartphone." Journal of Imaging 7, no. 8 (August 5, 2021): 136. http://dx.doi.org/10.3390/jimaging7080136.
Full textDissertations / Theses on the topic "Hyperspectral imaging"
Porter, Michael Anthony. "Hyperspectral imaging using ultraviolet light /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Dec%5FPorter.pdf.
Full textThesis Advisor(s): Richard C. Olsen, Christopher Brophy. Includes bibliographical references (p.55-56). Also available online.
Sjunnebo, Joakim. "Hyperspectral imaging for gas detection." Thesis, KTH, Tillämpad fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169623.
Full textPorter, Michael A. "Hyperspectral imaging using ultraviolet light." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/1817.
Full textJones, Julia Craven. "Infrared Hyperspectral Imaging Stokes Polarimeter." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145409.
Full textHartke, John. "DUAL BAND HYPERSPECTRAL IMAGING SPECTROMETER." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/195994.
Full textMAKKI, IHAB. "Hyperspectral Imaging for Landmine Detection." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2700516.
Full textNguyen, Dinh hoang. "Development of an optical system for preclinical molecular imaging of atherothrombosis." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCD062/document.
Full textIn this thesis work, we develop optical imaging protocols for the observation of then anoparticles on tissue slices in order to further link their localization and their “behaviour” to the biological pathological environment. Bimodal zinc and iron oxide-based MRI/optical nanoparticle contrast agents (Zn(Fe)O) have been synthesised with a novel azeotropicpolyol method in glycol solvents (DEG and PG). The most potent NPs, as regard to their MR contrast power, have been coated with carboxymethyl pullulan, polyethylene glycol,carboxymethyl dextran (CMD) and fucoidan, the latter being a polysaccharide able to specifically bind to the vascular wall. The coated NPs were injected into rat to locate atherothrombosis by MRI. Then the histological slices of harvested diseased tissue were imaged with our homemade optical microscope. Water removal using Dean-Stark apparatus is a novel strategy for the synthesis of NPs in polyol solution with high yield and small size.The NPs show the good magnetic and optical properties at room temperature. The coated nanoparticles were injected into an atherothrombotic rat model to locate the thrombus by MRI prior to sacrifice of the animals and tissue collection for histological study by optical microscopy. The difference of MRI images between before and after injection with Fucoidan-NPs and CMD-NPs is clear. The results indicated that fucoidan-NPs are linked to the thrombus. Some type of microscopies, such as fluorescent microscopy, dark field microscopy, hyperspectral dark field microscopy and interference dark field microscopy have been developed for the detection of NPs in liquid medium and in the histological tissue. By analyzing the spectrum of every pixel and comparing to the spectrum of reference materials, hyperspectral microscopy can detect the presence of nanomaterial on exposed tissue slices, locate, identify, and characterize them. Zn(Fe)O NPs would therefore constitute a potential bimodal contrast agent for MRI and optical imaging. Although many advance optical tools have been developed, but we found it is still a challenge to identify reliably the NPs in the tissue
Frontera, Pons Joana Maria. "Robust target detection for Hyperspectral Imaging." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0024/document.
Full textHyperspectral imaging (HSI) extends from the fact that for any given material, the amount of emitted radiation varies with wavelength. HSI sensors measure the radiance of the materials within each pixel area at a very large number of contiguous spectral bands and provide image data containing both spatial and spectral information. Classical adaptive detection schemes assume that the background is zero-mean Gaussian or with known mean vector that can be exploited. However, when the mean vector is unknown, as it is the case for hyperspectral imaging, it has to be included in the detection process. We propose in this work an extension of classical detection methods when both covariance matrix and mean vector are unknown.However, the actual multivariate distribution of the background pixels may differ from the generally used Gaussian hypothesis. The class of elliptical distributions has already been popularized for background characterization in HSI. Although these non-Gaussian models have been exploited for background modeling and detection schemes, the parameters estimation (covariance matrix, mean vector) is usually performed using classical Gaussian-based estimators. We analyze here some robust estimation procedures (M-estimators of location and scale) more suitable when non-Gaussian distributions are assumed. Jointly used with M-estimators, these new detectors allow to enhance the target detection performance in non-Gaussian environment while keeping the same performance than the classical detectors in Gaussian environment. Therefore, they provide a unified framework for target detection and anomaly detection in HSI
Yijian, Meng. "Extreme Ultraviolet Hyperspectral Coherent Diffractive Imaging." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/31928.
Full textAlabboud, Ied. "Human retinal oximetry using hyperspectral imaging." Thesis, Heriot-Watt University, 2009. http://hdl.handle.net/10399/2297.
Full textBooks on the topic "Hyperspectral imaging"
Chang, Chein-I. Hyperspectral Imaging. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6.
Full textAikio, Mauri. Hyperspectral prism-grating-prism imaging spectrograph. Espoo [Finland]: Technical Research Centre of Finland, 2001.
Find full textPark, Bosoon, and Renfu Lu, eds. Hyperspectral Imaging Technology in Food and Agriculture. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2836-1.
Full textSun, Da-Wen. Hyperspectral imaging for food quality analysis and control. London: Academic, 2010.
Find full textHyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers, 2003.
Find full textUnited States. National Aeronautics and Space Administration., ed. Planetary Hyperspectral Imager (PHI): PIDDP, final report. Danbury, CT: Hughes Danbury Optical Systems, 1996.
Find full textPedram, Ghamisi, ed. Spectral-spatial classififcation of hyperspectral remote sensing images. Boston: Artech House, 2015.
Find full textHans, Grahn, and Geladi Paul, eds. Techniques and applications of hyperspectral image analysis. Chichester, West Sussex, England: J. Wiley, 2007.
Find full textUnited States. National Aeronautics and Space Administration., ed. Programmable hyperspectral image mapper with on-array processing: [patent application]. [Washington, DC: National Aeronautics and Space Administration, 1992.
Find full textUnited States. National Aeronautics and Space Administration., ed. Programmable hyperspectral image mapper with on-array processing: [patent application]. [Washington, DC: National Aeronautics and Space Administration, 1992.
Find full textBook chapters on the topic "Hyperspectral imaging"
Gowen, A. A., E. Gaston, and J. Burger. "Hyperspectral Imaging." In Food Engineering Series, 199–216. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0311-5_9.
Full textNieves, Juan Luis. "Hyperspectral Imaging." In Encyclopedia of Color Science and Technology, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-27851-8_425-1.
Full textNieves, Juan Luis. "Hyperspectral Imaging." In Encyclopedia of Color Science and Technology, 910–17. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89862-5_425.
Full textChang, Chein-I. "Introduction." In Hyperspectral Imaging, 1–11. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_1.
Full textChang, Chein-I. "Target Abundance-Constrained Mixed Pixel Classification (TACMPC)." In Hyperspectral Imaging, 181–205. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_10.
Full textChang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers." In Hyperspectral Imaging, 207–27. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_11.
Full textChang, Chein-I. "Target Signature-Constrained Mixed Pixel Classification (TSCMPC): Linearly Constrained Discriminant Analysis (LCDA)." In Hyperspectral Imaging, 229–42. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_12.
Full textChang, Chein-I. "Automatic Mixed Pixel Classification (AMPC): Unsupervised Mixed Pixel Classification." In Hyperspectral Imaging, 245–55. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_13.
Full textChang, Chein-I. "Automatic Mixed Pixel Classificatio (AMPC): Anomaly Classification." In Hyperspectral Imaging, 257–75. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_14.
Full textChang, Chein-I. "Automatic mixed pixel classification (AMPC): Linear spectral random mixture analysis (LSRMA)." In Hyperspectral Imaging, 277–303. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9170-6_15.
Full textConference papers on the topic "Hyperspectral imaging"
Cui, Qi, and Liang Gao. "Compressive Hyperspectral Imaging." In Imaging Systems and Applications. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/isa.2023.im4e.6.
Full textDai, Qionghai, Chenguang Ma, Jinli Suo, and Xun Cao. "Computational Hyperspectral Imaging." In JSAP-OSA Joint Symposia. Washington, D.C.: OSA, 2014. http://dx.doi.org/10.1364/jsap.2014.20p_c4_5.
Full textChang, Chein-I. "Progressive hyperspectral imaging." In SPIE Remote Sensing, edited by Bormin Huang and Antonio J. Plaza. SPIE, 2012. http://dx.doi.org/10.1117/12.979188.
Full textNischan, Melissa L., Amy B. Newbury, Rose Joseph, Mrinal A. Iyengar, Berton C. Willard, Gary J. Swanson, Justin Libby, Bernadette Johnson, and Hsiao-hua K. Burke. "Active hyperspectral imaging." In International Symposium on Optical Science and Technology. SPIE, 2000. http://dx.doi.org/10.1117/12.406578.
Full textDescour, M. R., C. E. Volin, B. K. Ford, E. L. Dereniak, P. D. Maker, and D. W. Wilson. "Snapshot Hyperspectral Imaging." In Integrated Computational Imaging Systems. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/icis.2001.itha4.
Full textDescour, Michael, C. E. Volin, B. K. Ford, E. L. Dereniak, P. D. Maker, and D. W. Wilson. "Snapshot hyperspectral imaging." In Integrated Computational Imaging Systems. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/icis.2001.iwb4.
Full textDescour, Michael, C. E. Volin, B. K. Ford, E. L. Dereniak, P. D. Maker, and D. W. Wilson. "Snapshot hyperspectral imaging." In Integrated Computational Imaging Systems. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/icis.2001.wb4.
Full textMurzina, Marina V. A., and J. Paul Farrell. "Dynamic hyperspectral imaging." In Nondestructive Evaulation for Health Monitoring and Diagnostics, edited by Aaron A. Diaz, A. Emin Aktan, H. Felix Wu, Steven R. Doctor, and Yoseph Bar-Cohen. SPIE, 2005. http://dx.doi.org/10.1117/12.598620.
Full textStern, Adrian, August Yitzhak, Vladimir Farber, and Yair Rivenson. "Hyperspectral compressive imaging." In 2013 12th Workshop on Information Optics (WIO). IEEE, 2013. http://dx.doi.org/10.1109/wio.2013.6601243.
Full textCui, Qi, and Liang Gao. "Compressive hyperspectral imaging." In Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, edited by Liang Gao, Guoan Zheng, and Seung Ah Lee. SPIE, 2024. http://dx.doi.org/10.1117/12.3003319.
Full textReports on the topic "Hyperspectral imaging"
Gittins, Christopher M., William J. Marinelli, and Anthony J. Ratkowski. Airis Hyperspectral Imaging Technology,. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada329070.
Full textBissett, W. P. High Altitude Hyperspectral Imaging Spectroscopy. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada439987.
Full textDavis, Curtiss O. Hyperspectral Imaging of River Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada572752.
Full textDavis, Curtiss O. Hyperspectral Imaging of River Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557150.
Full textPokrzywinski, Kaytee, Cliff Morgan, Scott Bourne, Molly Reif, Kenneth Matheson, and Shea Hammond. A novel laboratory method for the detection and identification of cyanobacteria using hyperspectral imaging : hyperspectral imaging for cyanobacteria detection. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/40966.
Full textManolakis, D. Detection Algorithms for Hyperspectral Imaging Applications. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada399744.
Full textThiyanarantnam, Pradeep, Stanley Osher, Susan Chen, Wotao Yin, and Kevin Kelly. Compressive Hyperspectral Imaging and Anomaly Detection. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada580327.
Full textKwon, Heesung, Dalton Rosario, Neelam Gupta, Matthew Thielke, Dale Smith, Partick Rauss, Patti Gillespie, and Nasser M. Nasrabadi. Hyperspectral Imaging and Obstacle Detection for Robotics Navigation. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada485820.
Full textGrimm, David C., David W. Messinger, John P. Kerekes, and John R. Schott. Hybridization of Hyperspectral Imaging Target Detection Algorithm Chains. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada431819.
Full textWolf, Malima. Hyperspectral Imaging for the Identification of Light Metals. Office of Scientific and Technical Information (OSTI), June 2015. http://dx.doi.org/10.2172/1187882.
Full text