Relevant bibliographies by topics / Hyperspectral data analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hyperspectral data analysis'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Hyperspectral data analysis"

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Landgrebe, D. "Hyperspectral image data analysis." IEEE Signal Processing Magazine 19, no. 1 (2002): 17–28. http://dx.doi.org/10.1109/79.974718.

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Tsai, Fuan, and William Philpot. "Derivative Analysis of Hyperspectral Data." Remote Sensing of Environment 66, no. 1 (1998): 41–51. http://dx.doi.org/10.1016/s0034-4257(98)00032-7.

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Zaman, Zainab, Saad Bin Ahmed, and Muhammad Imran Malik. "Analysis of Hyperspectral Data to Develop an Approach for Document Images." Sensors 23, no. 15 (2023): 6845. http://dx.doi.org/10.3390/s23156845.

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Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, featu
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Qi, Beixun. "Hyperspectral Image Database Query Based on Big Data Analysis Technology." E3S Web of Conferences 275 (2021): 03018. http://dx.doi.org/10.1051/e3sconf/202127503018.

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In this paper, we extract spectral image features from a hyperspectral image database, and use big data technology to classify spectra hierarchically, to achieve the purpose of efficient database matching. In this paper, the LDMGI (local discriminant models and global integration) algorithm and big data branch definition algorithm are used to classify the features of the hyperspectral image and save the extracted feature data. Hierarchical color similarity is used to match the spectrum. By clustering colors, spectral information can be stored as chain nodes in the database, which can improve t
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Pervez, W., S. A. Khan, and Valiuddin. "HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 169–75. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-169-2015.

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Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is
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., Y. Sai Praveen. "IMAGE FUSION ANALYSIS FOR HYPERSPECTRAL DATA." International Journal of Research in Engineering and Technology 05, no. 19 (2016): 18–21. http://dx.doi.org/10.15623/ijret.2016.0519004.

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Burger, James, and Aoife Gowen. "Data handling in hyperspectral image analysis." Chemometrics and Intelligent Laboratory Systems 108, no. 1 (2011): 13–22. http://dx.doi.org/10.1016/j.chemolab.2011.04.001.

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Wu, Zebin, Jinping Gu, Yonglong Li, Fu Xiao, Jin Sun, and Zhihui Wei. "Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3252148.

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Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model
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Liu, Jing, Tingting Wang, and Yulong Qiao. "Depth and Width Changeable Network-Based Deep Kernel Learning-Based Hyperspectral Sensor Data Analysis." Wireless Communications and Mobile Computing 2021 (February 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/8842396.

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Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the m
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Zhu, Wei Hong, and Cheng Zhe Xu. "Hyperspectral Data Analysis for Detecting Lead Pollution in Rice." Applied Mechanics and Materials 433-435 (October 2013): 456–59. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.456.

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This paper presents a new method for detecting lead pollution in rice by analyzing hyperspectral data. First, preprocessing method is used to remove the outliers which deviate so much from other hyperspectral data. Then, dimensionality-reduced data are made by using discrete wavelet transform. Finally, linear discriminant analysis is utilized to extract the feature which characterizes polluted and unpolluted rice. The experimental result based on the proposed method shows the good performance in detecting lead pollution in rice.
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Dissertations / Theses on the topic "Hyperspectral data analysis"

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Petri, David Scott. "Analysis of hyperspectral data using polarimetric characteristics." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA327923.

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Thesis (M.S. in System Technology (Space Systems Operations)) Naval Postgraduate School, December 1996.<br>Thesis advisor(s): David D. Cleary, Richard Christopher Olsen. "December 1996." Includes bibliographical references (p. 77-78). Also Available online.
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Kelman, Timothy George Harold. "Techniques for capture and analysis of hyperspectral data." Thesis, University of Strathclyde, 2016. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=26434.

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The work presented in this thesis focusses on new techniques for capture and analysis of hyperspectral data. Due to the three-dimensional nature of hyperspectral data, image acquisition often requires some form of movement of either the object or the detector. This thesis presents a novel technique which utilises a rotational line-scan rather than a linear line-scan. Furthermore, a method for automatically calibrating this system using a calibration object is described. Compared with traditional linear scanning systems, the performance is shown to be high enough that a rotational scanning syst
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Fountanas, Leonidas. "Principal components based techniques for hyperspectral image data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FFountanas.pdf.

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Burger, James. "Hyperspectral NIR image analysis : data exploration, correction, and regression /." Umeå : Unit of Biomass Technology and Chemistry, Swedish University of Agricultural Sciences, 2006. http://epsilon.slu.se/200660.pdf.

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Fay, Matthew Edward. "An analysis of hyperspectral imagery data collected during Operation Desert Radiance." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA302643.

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Thesis (M.S. in Systems Technology (Space Systems Operations)) Naval Postgraduate School, June 1995.<br>"June 1995." Thesis advisor(s): Richard Christopher Olsen. Bibliography: p. 99-101. Also available online.
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Wahrman, Spencer A. "Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17473.

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Approved for public release; distribution is unlimited<br>Grand Lake, Colorado has experienced a severe mountain pine beetle outbreak over the past twenty years. The aim of this study was to map lodgepole pine mortality and health decline due to mountain pine beetle. Multispectral data spanning a five-year period from 2006 to 2011 were used to assess the progression from live, green trees to dead, gray-brown trees. IKONOS data from 2011 were corrected to reflectance and validated against an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 201
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Vikström, Axel. "A comparison of different machine learning algorithms applied to hyperspectral data analysis." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184538.

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Hyperspectral image analysis works with image data where each pixel contains hundreds of wavelengths acquired from spectral measurements. It is a growing field of research in the sciences and industries because it can distinguish visually similar objects. While many machine-learning methods work well for analysing regular images, little is known about how they perform on hyperspectral data. Standard methods for quantifying and classifying hyperspectral data include the chemometric methods PLS, PLS-DA and SIMCA. They provide rapid computations along with intuitive modelling and diagnostic tools
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Cheriyadat, Anil Meerasa. "Limitations of principal component analysis for dimensionality-reduction for classification of hyperspectral data." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-11072003-133109.

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PISCINI, ALESSANDRO. "Neural-Network approach to multispectral and hyperspectral data analysis for volcanic monitoring." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/214160.

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Dastile, Xolani Collen. "Improved tree species discrimination at leaf level with hyperspectral data combining binary classifiers." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002807.

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The purpose of the present thesis is to show that hyperspectral data can be used for discrimination between different tree species. The data set used in this study contains the hyperspectral measurements of leaves of seven savannah tree species. The data is high-dimensional and shows large within-class variability combined with small between-class variability which makes discrimination between the classes challenging. We employ two classification methods: G-nearest neighbour and feed-forward neural networks. For both methods, direct 7-class prediction results in high misclassification rates. H
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Books on the topic "Hyperspectral data analysis"

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Petri, David Scott. Analysis of hyperspectral data using polarimetric characteristics. Naval Postgraduate School, 1996.

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Landgrebe, D. A. Hyperspectral data analysis procedures with reduced sensitivity to noise. National Aeronautics and Space Administration, 1993.

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Landgrebe, D. A. Hyperspectral data analysis procedures with reduced sensitivity to noise. National Aeronautics and Space Administration, 1993.

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Sturgeon, Melissa A. Spectral and polarimetric analysis of hyperspectral data collected by an acousto-optic tunable filter system. Naval Postgraduate School, 1993.

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Rubtsov, Nickolai, Mikhail Alymov, Alexander Kalinin, Alexey Vinogradov, Alexey Rodionov, and Kirill Troshin. Remote studies of combustion and explosion processes based on optoelectronic methods. AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62876066a124d8.04785158.

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The main objective of this book is to acquaint the reader with the main modern problems of&#x0D; the multisensor data analysis and opportunities of the hyperspectral shooting being carried out in the&#x0D; wide range of wavelengths from ultraviolet to the infrared range, visualization of the fast combustion&#x0D; processes of flame propagation and flame acceleration, the limit phenomena at flame ignition and&#x0D; propagation. The book can be useful to students of the high courses and scientists dealing with problems&#x0D; of optical spectroscopy, vizualisation, digital recognizing images and
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Chen, Jie, Yingying Song, and Hengchao Li, eds. Processing and Analysis of Hyperspectral Data. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.78179.

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Hyperspectral data, analysis techniques, and applications. Bishen Singh Mahendra Pal Singh, 2011.

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Song, Yingying, and Hengchao Li. Processing and Analysis of Hyperspectral Data. IntechOpen, 2020.

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Analysis of Hyperspectral Data Using Polarimetric Characteristics. Storming Media, 1996.

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Hyperspectral Data Processing: Algorithm Design and Analysis. Wiley & Sons Canada, Limited, John, 2013.

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Book chapters on the topic "Hyperspectral data analysis"

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Pu, Ruiliang. "Hyperspectral Data Analysis Techniques." In Hyperspectral Remote Sensing. CRC Press, 2017. http://dx.doi.org/10.1201/9781315120607-5.

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Richards, John A., and Xiuping Jia. "Interpretation of Hyperspectral Image Data." In Remote Sensing Digital Image Analysis. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_13.

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Ksieniewicz, Paweł, Bartosz Krawczyk, and Michał Woźniak. "Ensemble of One-Dimensional Classifiers for Hyperspectral Image Analysis." In Data Mining and Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_52.

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Zhu, Wei, Qian Du, and James E. Fowler. "Hyperspectral Image Compression Using Segmented Principal Component Analysis." In Satellite Data Compression. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1183-3_11.

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Safavi, Haleh, Chein-I. Chang, and Antonio J. Plaza. "Projection Pursuit-Based Dimensionality Reduction for Hyperspectral Analysis." In Satellite Data Compression. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1183-3_14.

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Gillis, David B., and Jeffrey H. Bowles. "An Introduction to Hyperspectral Image Data Modeling." In Excursions in Harmonic Analysis, Volume 1. Birkhäuser Boston, 2012. http://dx.doi.org/10.1007/978-0-8176-8376-4_9.

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Jun, Goo, and Joydeep Ghosh. "Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_5.

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Lucini, María M., and Alejandro C. Frery. "Robust Principal Components for Hyperspectral Data Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02611-9_13.

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Geladi, Paul, Hans Grahn, and Marena Manley. "Data Analysis and Chemometrics for Hyperspectral Imaging." In Raman, Infrared, and Near-Infrared Chemical Imaging. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470768150.ch5.

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Robila, Stefan A. "Independent Component Analysis." In Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05605-9_5.

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Conference papers on the topic "Hyperspectral data analysis"

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Granger, Margaret E., Abigail J. Latin, and Jeremy A. Johnson. "Significant Improvements to Terahertz Hyperspectral Image Analysis by Using Full-Field and Time-Domain Data." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.ath4a.4.

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We demonstrate improvements in terahertz (THz) hyperspectral image analysis by incorporating full-field THz data. We also show for the first time that THz hyperspectral analysis can be performed in the time-domain.
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Hall, Natalie C., and Carl J. Legleiter. "Hyperspectral microscopy methods for building a spectral reference data set to support Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms (SMASH)." In Optical Sensors. Optica Publishing Group, 2024. https://doi.org/10.1364/sensors.2024.stu1h.5.

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This study provides a proof-of-concept case study where cyanobacteria genera were differentiated using space-based hyperspectral imagery and a reference spectral dataset. Methods are also introduced to expand upon the reference spectral data using hyperspectral microscopy.
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Tsai, Fuan, and William D. Philpot. "Derivative analysis of hyperspectral data." In Satellite Remote Sensing III, edited by Daniel Arroyo-Bishop, Roberto Carla, Joan B. Lurie, et al. SPIE, 1996. http://dx.doi.org/10.1117/12.262471.

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Kruse, Fred A. "Expert system analysis of hyperspectral data." In SPIE Defense and Security Symposium, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2008. http://dx.doi.org/10.1117/12.767554.

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Aldeghlawi, Maher, Mohammed Q. Alkhatib, and Miguel Velez-Reyes. "Data summarization for hyperspectral image analysis." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2021. http://dx.doi.org/10.1117/12.2590762.

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Landgrebe, David A. "Analysis of multispectral and hyperspectral data." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Michael R. Descour, Jonathan M. Mooney, David L. Perry, and Luanna R. Illing. SPIE, 1995. http://dx.doi.org/10.1117/12.210898.

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Gamba, Paolo, Antonio J. Plaza, Jon A. Benediktsson, and Jocelyn Chanussot. "European perspectives in hyperspectral data analysis." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423933.

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Chen, Yu-Jung, Noel Richard, Hilda Deborah, Aurelie Tournie, Anne Michelin, and Christine Andraud. "Statistics of hyperspectral data/image analysis: Entropy." In 2018 Colour and Visual Computing Symposium (CVCS). IEEE, 2018. http://dx.doi.org/10.1109/cvcs.2018.8496573.

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Linderhed, Anna, Niclas Wadströmer, K. G. Stenborg, and Harald Nautsch. "Compression of hyperspectral data for automated analysis." In SPIE Europe Remote Sensing, edited by Lorenzo Bruzzone, Claudia Notarnicola, and Francesco Posa. SPIE, 2009. http://dx.doi.org/10.1117/12.830343.

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Bayliss, Jessica D., J. Anthony Gualtieri, and Robert F. Cromp. "Analyzing hyperspectral data with independent component analysis." In 26th AIPR Workshop: Exploiting New Image Sources and Sensors, edited by J. Michael Selander. SPIE, 1998. http://dx.doi.org/10.1117/12.300050.

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Reports on the topic "Hyperspectral data analysis"

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Jimenez, Luis O., Miguel Velez, and Shawn Hunt. Unsupervised Classification System for Hyperspectral Data Analysis. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada398803.

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Philpot, William. Analysis of Hyperspectral Coastal Ocean Color Data. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada634347.

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Philpot, William. Analysis of Hyperspectral Data for Coastal Bathymetry and Water Quality. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada624949.

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Yoon, Hong-Jun, Daniel Hopp, Kellen Leland, Ryan Prout, Paul Bryant, and Stanton Martin. HyperKube: A Kubernetes based system for the automation of processing and analysis of hyperspectral data obtained from multiple hyperspectral imaging systems. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2305378.

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Yoon, Hong-Jun, Dan Hopp, Kellen Leland, Ryan Prout, Paul Bryant, and Stanton Martin. HyperKube: A Kubernetes based system for the automation of processing and analysis of hyperspectral data obtained from multiple hyperspectral imaging systems. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2305800.

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HAALAND, DAVID M., MARK H. VAN BENTHEM, CHRISTINE M. WEHLBURG, and KOEHLER,IV, FREDERICK W. Reducing System Artifacts in Hyperspectral Image Data Analysis with the Use of Estimates of the Error Covariance in the Data. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/791897.

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Zimmerman, Richard C. Impact of Submerged Aquatic Vegetation on Radiative Transfer and Hyperspectral Remote Sensing in Optically Shallow Waters: Continued Analysis of CoBOP Data. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada626459.

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Douglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41222.

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The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. V
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Jackson, Samuel, Christina Saltus, Molly Reif, and Glenn Suir. During Nearshore Event Vegetation Gradation (DUNEVEG) : geospatial tools for automating remote vegetation extraction. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47649.

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Monitoring and modeling of coastal vegetation and ecosystems are major challenges, especially when considering environmental response to hazards, disturbances, and management activities. Remote sensing applications can provide alternatives and complementary approaches to the often costly and laborious field-based collection methods traditionally used for coastal ecosystem monitoring. New and improved sensors and data analysis techniques have become available, making remote sensing applications attractive for evaluation and potential use in monitoring coastal vegetation properties and ecosystem
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, contro
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