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You searched for subject:(hyperspectral imagery). Showing records 1 – 30 of 62 total matches.

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Virginia Tech

1. Koehn, Tabitha. A Hyperspectral Imager for a Cubesat to Identify Ocean Ship Parameters.

Degree: MS, Electrical Engineering, 2017, Virginia Tech

 A Hyperspectral imager aboard a cubesat would be able to provide images which could be used to identify ships and determine the ship's length and… (more)

Subjects/Keywords: Hyperspectral Imagery; Ship Detection; Instrumentation

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APA (6th Edition):

Koehn, T. (2017). A Hyperspectral Imager for a Cubesat to Identify Ocean Ship Parameters. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78880

Chicago Manual of Style (16th Edition):

Koehn, Tabitha. “A Hyperspectral Imager for a Cubesat to Identify Ocean Ship Parameters.” 2017. Masters Thesis, Virginia Tech. Accessed January 16, 2021. http://hdl.handle.net/10919/78880.

MLA Handbook (7th Edition):

Koehn, Tabitha. “A Hyperspectral Imager for a Cubesat to Identify Ocean Ship Parameters.” 2017. Web. 16 Jan 2021.

Vancouver:

Koehn T. A Hyperspectral Imager for a Cubesat to Identify Ocean Ship Parameters. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/10919/78880.

Council of Science Editors:

Koehn T. A Hyperspectral Imager for a Cubesat to Identify Ocean Ship Parameters. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/78880


Mississippi State University

2. Raksuntorn, Nareenart. UNSUPERVISED SPECTRAL MIXTURE ANALYSIS FOR HYPERSPECTRAL IMAGERY.

Degree: PhD, Electrical and Computer Engineering, 2009, Mississippi State University

 The objective of this dissertation is to investigate all the necessary components in spectral mixture analysis (SMA) for hyperspectral imagery under an unsupervised circumstance. When… (more)

Subjects/Keywords: Hyperspectral imagery; Spectral mixture analysis

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APA (6th Edition):

Raksuntorn, N. (2009). UNSUPERVISED SPECTRAL MIXTURE ANALYSIS FOR HYPERSPECTRAL IMAGERY. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-04192009-142516/ ;

Chicago Manual of Style (16th Edition):

Raksuntorn, Nareenart. “UNSUPERVISED SPECTRAL MIXTURE ANALYSIS FOR HYPERSPECTRAL IMAGERY.” 2009. Doctoral Dissertation, Mississippi State University. Accessed January 16, 2021. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04192009-142516/ ;.

MLA Handbook (7th Edition):

Raksuntorn, Nareenart. “UNSUPERVISED SPECTRAL MIXTURE ANALYSIS FOR HYPERSPECTRAL IMAGERY.” 2009. Web. 16 Jan 2021.

Vancouver:

Raksuntorn N. UNSUPERVISED SPECTRAL MIXTURE ANALYSIS FOR HYPERSPECTRAL IMAGERY. [Internet] [Doctoral dissertation]. Mississippi State University; 2009. [cited 2021 Jan 16]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-04192009-142516/ ;.

Council of Science Editors:

Raksuntorn N. UNSUPERVISED SPECTRAL MIXTURE ANALYSIS FOR HYPERSPECTRAL IMAGERY. [Doctoral Dissertation]. Mississippi State University; 2009. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-04192009-142516/ ;

3. Zhang, Kongwen. Exploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization.

Degree: PhD, Earth & Space Science, 2015, York University

 This dissertation describes three contributions in the characterization of vegetation canopies using remote sensing data, with a focus on hyperspectral and very high spatial resolution… (more)

Subjects/Keywords: Remote sensing; Forestry; Remote Sensing; Hyperspectral; High spatial resolution imagery

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APA (6th Edition):

Zhang, K. (2015). Exploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization. (Doctoral Dissertation). York University. Retrieved from http://hdl.handle.net/10315/29915

Chicago Manual of Style (16th Edition):

Zhang, Kongwen. “Exploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization.” 2015. Doctoral Dissertation, York University. Accessed January 16, 2021. http://hdl.handle.net/10315/29915.

MLA Handbook (7th Edition):

Zhang, Kongwen. “Exploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization.” 2015. Web. 16 Jan 2021.

Vancouver:

Zhang K. Exploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization. [Internet] [Doctoral dissertation]. York University; 2015. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/10315/29915.

Council of Science Editors:

Zhang K. Exploring Hyperspectral and Very High Spatial Resolution Imagery in Vegetation Characterization. [Doctoral Dissertation]. York University; 2015. Available from: http://hdl.handle.net/10315/29915


University of Minnesota

4. Moghimi, Ali. Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat.

Degree: PhD, Bioproducts/Biosystems Science Engineering and Management, 2019, University of Minnesota

 The present dissertation was motivated by the need to apply innovative technologies, automation, and artificial intelligence to agriculture in order to promote crop production while… (more)

Subjects/Keywords: Aerial imagery; Deep learning; Hyperspectral imaging; Machine learning; Plant phenotyping; Wheat

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APA (6th Edition):

Moghimi, A. (2019). Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/202435

Chicago Manual of Style (16th Edition):

Moghimi, Ali. “Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat.” 2019. Doctoral Dissertation, University of Minnesota. Accessed January 16, 2021. http://hdl.handle.net/11299/202435.

MLA Handbook (7th Edition):

Moghimi, Ali. “Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat.” 2019. Web. 16 Jan 2021.

Vancouver:

Moghimi A. Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/11299/202435.

Council of Science Editors:

Moghimi A. Integrating Hyperspectral Imaging and Artificial Intelligence to Develop Automated Frameworks for High-throughput Phenotyping in Wheat. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/202435


University of Maryland

5. Duke, Kevin W. A Study of the Relationship Between Spectrum and Geometry Through Fourier Frames and Laplacian Eigenmaps.

Degree: Mathematics, 2012, University of Maryland

 This thesis has two parts. The first part is a study of Fourier frames. We follow the development of the theory, beginning with its classical… (more)

Subjects/Keywords: Mathematics; Cantor measures; Fourier frames; hyperspectral imagery; Laplacian Eigenmaps; sampling theory

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APA (6th Edition):

Duke, K. W. (2012). A Study of the Relationship Between Spectrum and Geometry Through Fourier Frames and Laplacian Eigenmaps. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/13260

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Duke, Kevin W. “A Study of the Relationship Between Spectrum and Geometry Through Fourier Frames and Laplacian Eigenmaps.” 2012. Thesis, University of Maryland. Accessed January 16, 2021. http://hdl.handle.net/1903/13260.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Duke, Kevin W. “A Study of the Relationship Between Spectrum and Geometry Through Fourier Frames and Laplacian Eigenmaps.” 2012. Web. 16 Jan 2021.

Vancouver:

Duke KW. A Study of the Relationship Between Spectrum and Geometry Through Fourier Frames and Laplacian Eigenmaps. [Internet] [Thesis]. University of Maryland; 2012. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/1903/13260.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Duke KW. A Study of the Relationship Between Spectrum and Geometry Through Fourier Frames and Laplacian Eigenmaps. [Thesis]. University of Maryland; 2012. Available from: http://hdl.handle.net/1903/13260

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

6. Manjunatha Bharadwaj, Sandhya. Land Cover Quantification using Autoencoder based Unsupervised Deep Learning.

Degree: MS, Computer Engineering, 2020, Virginia Tech

 This work aims to develop an automated deep learning model for identifying and estimating the composition of the different land covers in a region using… (more)

Subjects/Keywords: Deep Learning; Autoencoder; Land Cover; Hyperspectral Imagery; Spectral Unmixing; Reflectance Spectra

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APA (6th Edition):

Manjunatha Bharadwaj, S. (2020). Land Cover Quantification using Autoencoder based Unsupervised Deep Learning. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99861

Chicago Manual of Style (16th Edition):

Manjunatha Bharadwaj, Sandhya. “Land Cover Quantification using Autoencoder based Unsupervised Deep Learning.” 2020. Masters Thesis, Virginia Tech. Accessed January 16, 2021. http://hdl.handle.net/10919/99861.

MLA Handbook (7th Edition):

Manjunatha Bharadwaj, Sandhya. “Land Cover Quantification using Autoencoder based Unsupervised Deep Learning.” 2020. Web. 16 Jan 2021.

Vancouver:

Manjunatha Bharadwaj S. Land Cover Quantification using Autoencoder based Unsupervised Deep Learning. [Internet] [Masters thesis]. Virginia Tech; 2020. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/10919/99861.

Council of Science Editors:

Manjunatha Bharadwaj S. Land Cover Quantification using Autoencoder based Unsupervised Deep Learning. [Masters Thesis]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99861


University of Sydney

7. Uezato, Tatsumi. Unmixing of hyperspectral data by incorporating spectral variability and spatial information .

Degree: 2016, University of Sydney

 Spectral unmixing enables quantitative information on the abundances of cover types to be estimated within each image pixel. Although many spectral unmixing methods have been… (more)

Subjects/Keywords: Hyperspectral imagery; spectral unmixing; endmember; image processing; machine learning

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APA (6th Edition):

Uezato, T. (2016). Unmixing of hyperspectral data by incorporating spectral variability and spatial information . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/15929

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Uezato, Tatsumi. “Unmixing of hyperspectral data by incorporating spectral variability and spatial information .” 2016. Thesis, University of Sydney. Accessed January 16, 2021. http://hdl.handle.net/2123/15929.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Uezato, Tatsumi. “Unmixing of hyperspectral data by incorporating spectral variability and spatial information .” 2016. Web. 16 Jan 2021.

Vancouver:

Uezato T. Unmixing of hyperspectral data by incorporating spectral variability and spatial information . [Internet] [Thesis]. University of Sydney; 2016. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/2123/15929.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Uezato T. Unmixing of hyperspectral data by incorporating spectral variability and spatial information . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/15929

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Utah State University

8. Pound, Andrew E. Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery.

Degree: MS, Electrical and Computer Engineering, 2014, Utah State University

Hyperspectral imaging (HSI) produces spatial images with pixels that, instead of consisting of three colors, consist of hundreds of spectral measurements. Because there are… (more)

Subjects/Keywords: Exploiting Sparsity; Dictionary Learning; Hyperspectral Imagery; Electrical and Computer Engineering

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APA (6th Edition):

Pound, A. E. (2014). Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/4020

Chicago Manual of Style (16th Edition):

Pound, Andrew E. “Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery.” 2014. Masters Thesis, Utah State University. Accessed January 16, 2021. https://digitalcommons.usu.edu/etd/4020.

MLA Handbook (7th Edition):

Pound, Andrew E. “Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery.” 2014. Web. 16 Jan 2021.

Vancouver:

Pound AE. Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery. [Internet] [Masters thesis]. Utah State University; 2014. [cited 2021 Jan 16]. Available from: https://digitalcommons.usu.edu/etd/4020.

Council of Science Editors:

Pound AE. Exploiting Sparsity and Dictionary Learning to Efficiently Classify Materials in Hyperspectral Imagery. [Masters Thesis]. Utah State University; 2014. Available from: https://digitalcommons.usu.edu/etd/4020


University of Minnesota

9. Nigon, Tyler John. Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop.

Degree: MS, Soil Science (Land and atmospheric science), 2012, University of Minnesota

University of Minnesota M.S. thesis. November 2012. Major: Soil Science (Land and atmospheric science). Advisors: Carl J. Rosen and David J. Mulla. 1 computer file… (more)

Subjects/Keywords: Chlorophyll meter; Hyperspectral imagery; Nitrogen stress; Nitrogen sufficiency index; Potato; Water stress

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APA (6th Edition):

Nigon, T. J. (2012). Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop. (Masters Thesis). University of Minnesota. Retrieved from http://purl.umn.edu/143695

Chicago Manual of Style (16th Edition):

Nigon, Tyler John. “Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop.” 2012. Masters Thesis, University of Minnesota. Accessed January 16, 2021. http://purl.umn.edu/143695.

MLA Handbook (7th Edition):

Nigon, Tyler John. “Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop.” 2012. Web. 16 Jan 2021.

Vancouver:

Nigon TJ. Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop. [Internet] [Masters thesis]. University of Minnesota; 2012. [cited 2021 Jan 16]. Available from: http://purl.umn.edu/143695.

Council of Science Editors:

Nigon TJ. Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop. [Masters Thesis]. University of Minnesota; 2012. Available from: http://purl.umn.edu/143695


University of Cambridge

10. Lee, Juheon. Mapping individual trees from airborne multi-sensor imagery.

Degree: PhD, 2016, University of Cambridge

 Airborne multi-sensor imaging is increasingly used to examine vegetation properties. The advantage of using multiple types of sensor is that each detects a different feature… (more)

Subjects/Keywords: 621.36; Remote Sensing; Image Processing; Image segmentation; Image registration; Tree species detection; LiDAR; Hyperspectral Imagery

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APA (6th Edition):

Lee, J. (2016). Mapping individual trees from airborne multi-sensor imagery. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.12763 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723521

Chicago Manual of Style (16th Edition):

Lee, Juheon. “Mapping individual trees from airborne multi-sensor imagery.” 2016. Doctoral Dissertation, University of Cambridge. Accessed January 16, 2021. https://doi.org/10.17863/CAM.12763 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723521.

MLA Handbook (7th Edition):

Lee, Juheon. “Mapping individual trees from airborne multi-sensor imagery.” 2016. Web. 16 Jan 2021.

Vancouver:

Lee J. Mapping individual trees from airborne multi-sensor imagery. [Internet] [Doctoral dissertation]. University of Cambridge; 2016. [cited 2021 Jan 16]. Available from: https://doi.org/10.17863/CAM.12763 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723521.

Council of Science Editors:

Lee J. Mapping individual trees from airborne multi-sensor imagery. [Doctoral Dissertation]. University of Cambridge; 2016. Available from: https://doi.org/10.17863/CAM.12763 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723521


University of Cambridge

11. Lee, Juheon. Mapping individual trees from airborne multi-sensor imagery.

Degree: PhD, 2016, University of Cambridge

 Airborne multi-sensor imaging is increasingly used to examine vegetation properties. The advantage of using multiple types of sensor is that each detects a different feature… (more)

Subjects/Keywords: Remote Sensing; Image Processing; Image segmentation; Image registration; Tree species detection; LiDAR; Hyperspectral Imagery

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lee, J. (2016). Mapping individual trees from airborne multi-sensor imagery. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/266686

Chicago Manual of Style (16th Edition):

Lee, Juheon. “Mapping individual trees from airborne multi-sensor imagery.” 2016. Doctoral Dissertation, University of Cambridge. Accessed January 16, 2021. https://www.repository.cam.ac.uk/handle/1810/266686.

MLA Handbook (7th Edition):

Lee, Juheon. “Mapping individual trees from airborne multi-sensor imagery.” 2016. Web. 16 Jan 2021.

Vancouver:

Lee J. Mapping individual trees from airborne multi-sensor imagery. [Internet] [Doctoral dissertation]. University of Cambridge; 2016. [cited 2021 Jan 16]. Available from: https://www.repository.cam.ac.uk/handle/1810/266686.

Council of Science Editors:

Lee J. Mapping individual trees from airborne multi-sensor imagery. [Doctoral Dissertation]. University of Cambridge; 2016. Available from: https://www.repository.cam.ac.uk/handle/1810/266686


George Mason University

12. Foster, Kyle A. Measurements of the Opposition Effect in the Visible and Near-Infrared Using an Improvised Imaging Spectropolarimeter .

Degree: 2013, George Mason University

 Spectropolarimetry is a remote sensing technique that combines both spectral and polarimetric measurements. It is already well-established in the astronomical community as an indispensable tool,… (more)

Subjects/Keywords: spectropolarimetry; polarimetric imaging; hyperspectral imaging; Stokes imagery; opposition effect; visible and near-infrared

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APA (6th Edition):

Foster, K. A. (2013). Measurements of the Opposition Effect in the Visible and Near-Infrared Using an Improvised Imaging Spectropolarimeter . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8001

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Foster, Kyle A. “Measurements of the Opposition Effect in the Visible and Near-Infrared Using an Improvised Imaging Spectropolarimeter .” 2013. Thesis, George Mason University. Accessed January 16, 2021. http://hdl.handle.net/1920/8001.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Foster, Kyle A. “Measurements of the Opposition Effect in the Visible and Near-Infrared Using an Improvised Imaging Spectropolarimeter .” 2013. Web. 16 Jan 2021.

Vancouver:

Foster KA. Measurements of the Opposition Effect in the Visible and Near-Infrared Using an Improvised Imaging Spectropolarimeter . [Internet] [Thesis]. George Mason University; 2013. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/1920/8001.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Foster KA. Measurements of the Opposition Effect in the Visible and Near-Infrared Using an Improvised Imaging Spectropolarimeter . [Thesis]. George Mason University; 2013. Available from: http://hdl.handle.net/1920/8001

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

13. Nigon, Tyler John. Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop.

Degree: MS, Soil Science (Land and atmospheric science), 2012, University of Minnesota

 Post-emergence nitrogen (N) fertilizer is typically split applied to irrigated potato (Solanum tuberosum L.) in Minnesota in order to minimize the likelihood of nitrate leaching… (more)

Subjects/Keywords: Chlorophyll meter; Hyperspectral imagery; Nitrogen stress; Nitrogen sufficiency index; Potato; Water stress

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APA (6th Edition):

Nigon, T. J. (2012). Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop. (Masters Thesis). University of Minnesota. Retrieved from http://purl.umn.edu/143695

Chicago Manual of Style (16th Edition):

Nigon, Tyler John. “Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop.” 2012. Masters Thesis, University of Minnesota. Accessed January 16, 2021. http://purl.umn.edu/143695.

MLA Handbook (7th Edition):

Nigon, Tyler John. “Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop.” 2012. Web. 16 Jan 2021.

Vancouver:

Nigon TJ. Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop. [Internet] [Masters thesis]. University of Minnesota; 2012. [cited 2021 Jan 16]. Available from: http://purl.umn.edu/143695.

Council of Science Editors:

Nigon TJ. Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop. [Masters Thesis]. University of Minnesota; 2012. Available from: http://purl.umn.edu/143695


Colorado State University

14. Chepushtanova, Sofya. Algorithms for feature selection and pattern recognition on Grassmann manifolds.

Degree: PhD, Mathematics, 2015, Colorado State University

 This dissertation presents three distinct application-driven research projects united by ideas and topics from geometric data analysis, optimization, computational topology, and machine learning. We first… (more)

Subjects/Keywords: Grassmann manifold; pattern recognition; sparse support vector machines; hyperspectral imagery; feature selection; persistent homology

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APA (6th Edition):

Chepushtanova, S. (2015). Algorithms for feature selection and pattern recognition on Grassmann manifolds. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/167225

Chicago Manual of Style (16th Edition):

Chepushtanova, Sofya. “Algorithms for feature selection and pattern recognition on Grassmann manifolds.” 2015. Doctoral Dissertation, Colorado State University. Accessed January 16, 2021. http://hdl.handle.net/10217/167225.

MLA Handbook (7th Edition):

Chepushtanova, Sofya. “Algorithms for feature selection and pattern recognition on Grassmann manifolds.” 2015. Web. 16 Jan 2021.

Vancouver:

Chepushtanova S. Algorithms for feature selection and pattern recognition on Grassmann manifolds. [Internet] [Doctoral dissertation]. Colorado State University; 2015. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/10217/167225.

Council of Science Editors:

Chepushtanova S. Algorithms for feature selection and pattern recognition on Grassmann manifolds. [Doctoral Dissertation]. Colorado State University; 2015. Available from: http://hdl.handle.net/10217/167225


RMIT University

15. Robinson, K. Integrating HYMAP airborne hyperspectral data and field-based spectrometer data to map arid zone vegetation.

Degree: 2008, RMIT University

 During 2002, the Australian Department of Defence hosted a high explosives trial at the Woomera Large Scale Explosives Test Area (LSETA) in South Australia. HyMap®… (more)

Subjects/Keywords: Fields of Research; Australia; South Australia; Woomera; vegetation classification; mapping; vegetation; arid zones; hyperspectral; HYMAP; imagery

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APA (6th Edition):

Robinson, K. (2008). Integrating HYMAP airborne hyperspectral data and field-based spectrometer data to map arid zone vegetation. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:7508

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Robinson, K. “Integrating HYMAP airborne hyperspectral data and field-based spectrometer data to map arid zone vegetation.” 2008. Thesis, RMIT University. Accessed January 16, 2021. http://researchbank.rmit.edu.au/view/rmit:7508.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Robinson, K. “Integrating HYMAP airborne hyperspectral data and field-based spectrometer data to map arid zone vegetation.” 2008. Web. 16 Jan 2021.

Vancouver:

Robinson K. Integrating HYMAP airborne hyperspectral data and field-based spectrometer data to map arid zone vegetation. [Internet] [Thesis]. RMIT University; 2008. [cited 2021 Jan 16]. Available from: http://researchbank.rmit.edu.au/view/rmit:7508.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Robinson K. Integrating HYMAP airborne hyperspectral data and field-based spectrometer data to map arid zone vegetation. [Thesis]. RMIT University; 2008. Available from: http://researchbank.rmit.edu.au/view/rmit:7508

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

16. Zhu, Wei. PCA AND JPEG2000-BASED LOSSY COMPRESSION FOR HYPERSPECTRAL IMAGERY.

Degree: PhD, Electrical and Computer Engineering, 2011, Mississippi State University

  This dissertation develops several new algorithms to solve existing problems in practical application of the previously developed PCA+JPEG2000, which has shown superior rate-distortion performance… (more)

Subjects/Keywords: Hyperspectral Imagery; JPEG2000; PCA; Lossy Compression

Hyperspectral Imagery ........................................ 91 6.2 Proposed Compression Algorithms… …major distortion, leading to much higher compression ratios. For hyperspectral imagery… …algorithm has been successfully applied to hyperspectral imagery [58, 59]. It is now… …8 2.1 General Hyperspectral Compression Techniques ..................................... 8… …2.2 Hyperspectral Image Compression Based on PCA and JPEG2000 ....... 16 2.2.1 PCA… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhu, W. (2011). PCA AND JPEG2000-BASED LOSSY COMPRESSION FOR HYPERSPECTRAL IMAGERY. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-03082011-202548/ ;

Chicago Manual of Style (16th Edition):

Zhu, Wei. “PCA AND JPEG2000-BASED LOSSY COMPRESSION FOR HYPERSPECTRAL IMAGERY.” 2011. Doctoral Dissertation, Mississippi State University. Accessed January 16, 2021. http://sun.library.msstate.edu/ETD-db/theses/available/etd-03082011-202548/ ;.

MLA Handbook (7th Edition):

Zhu, Wei. “PCA AND JPEG2000-BASED LOSSY COMPRESSION FOR HYPERSPECTRAL IMAGERY.” 2011. Web. 16 Jan 2021.

Vancouver:

Zhu W. PCA AND JPEG2000-BASED LOSSY COMPRESSION FOR HYPERSPECTRAL IMAGERY. [Internet] [Doctoral dissertation]. Mississippi State University; 2011. [cited 2021 Jan 16]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03082011-202548/ ;.

Council of Science Editors:

Zhu W. PCA AND JPEG2000-BASED LOSSY COMPRESSION FOR HYPERSPECTRAL IMAGERY. [Doctoral Dissertation]. Mississippi State University; 2011. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03082011-202548/ ;


Rochester Institute of Technology

17. Bishoff, Josef. Target detection using oblique hyperspectral imagery: A Domain trade study.

Degree: Chester F. Carlson Center for Imaging Science (COS), 2008, Rochester Institute of Technology

Hyperspectral imagery (HSI) has proven to be a useful tool when considering the task of target detection. Various processes have been developed that manipulate HSI… (more)

Subjects/Keywords: HSI data; Hyperspectral imagery; Spectral signature; Target detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bishoff, J. (2008). Target detection using oblique hyperspectral imagery: A Domain trade study. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/2888

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bishoff, Josef. “Target detection using oblique hyperspectral imagery: A Domain trade study.” 2008. Thesis, Rochester Institute of Technology. Accessed January 16, 2021. https://scholarworks.rit.edu/theses/2888.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bishoff, Josef. “Target detection using oblique hyperspectral imagery: A Domain trade study.” 2008. Web. 16 Jan 2021.

Vancouver:

Bishoff J. Target detection using oblique hyperspectral imagery: A Domain trade study. [Internet] [Thesis]. Rochester Institute of Technology; 2008. [cited 2021 Jan 16]. Available from: https://scholarworks.rit.edu/theses/2888.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bishoff J. Target detection using oblique hyperspectral imagery: A Domain trade study. [Thesis]. Rochester Institute of Technology; 2008. Available from: https://scholarworks.rit.edu/theses/2888

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Texas A&M University

18. Sakla, Wesam Adel. Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery.

Degree: PhD, Electrical Engineering, 2011, Texas A&M University

 A fundamental challenge in target detection in hyperspectral imagery is spectral variability. In target detection applications, we are provided with a pure target signature; we… (more)

Subjects/Keywords: hyperspectral imagery; target detection; support vector data description; spectral fringe-adjusted joint transform correlation; discrete wavelet transform; golden-section search algorithm

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APA (6th Edition):

Sakla, W. A. (2011). Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7526

Chicago Manual of Style (16th Edition):

Sakla, Wesam Adel. “Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery.” 2011. Doctoral Dissertation, Texas A&M University. Accessed January 16, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7526.

MLA Handbook (7th Edition):

Sakla, Wesam Adel. “Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery.” 2011. Web. 16 Jan 2021.

Vancouver:

Sakla WA. Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery. [Internet] [Doctoral dissertation]. Texas A&M University; 2011. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7526.

Council of Science Editors:

Sakla WA. Novel Pattern Recognition Techniques for Improved Target Detection in Hyperspectral Imagery. [Doctoral Dissertation]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7526


INP Toulouse

19. Altmann, Yoann. Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales.

Degree: Docteur es, Image, Information, Hypermedia, 2013, INP Toulouse

 Le démélange spectral est un des sujets majeurs de l’analyse d’images hyperspectrales. Ce problème consiste à identifier les composants macroscopiques présents dans une image hyperspectrale… (more)

Subjects/Keywords: Imagerie hyperspectrale; Démélange spectral; Estimation bayésienne; Modèles non-linéaires; Hyperspectral imagery; Spectral unmixing; Bayesian estimation; Nonlinear models

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APA (6th Edition):

Altmann, Y. (2013). Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales. (Doctoral Dissertation). INP Toulouse. Retrieved from http://www.theses.fr/2013INPT0084

Chicago Manual of Style (16th Edition):

Altmann, Yoann. “Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales.” 2013. Doctoral Dissertation, INP Toulouse. Accessed January 16, 2021. http://www.theses.fr/2013INPT0084.

MLA Handbook (7th Edition):

Altmann, Yoann. “Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales.” 2013. Web. 16 Jan 2021.

Vancouver:

Altmann Y. Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales. [Internet] [Doctoral dissertation]. INP Toulouse; 2013. [cited 2021 Jan 16]. Available from: http://www.theses.fr/2013INPT0084.

Council of Science Editors:

Altmann Y. Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales. [Doctoral Dissertation]. INP Toulouse; 2013. Available from: http://www.theses.fr/2013INPT0084

20. Chen, Bai Yang. Partitionnement non supervisé d'images hyperspectrales : application à l'identification de la végétation littorale : Unsupervised partitioning approach of hyperspectral image : application to the identification of the algal vegetation.

Degree: Docteur es, Traitement du signal et télécommunications, 2016, Rennes 1

La première partie de ce travail présente un état de l'art des principaux critères non supervisés, non paramétriques, d'évaluation d'une partition, des méthodes d'estimation préliminaires… (more)

Subjects/Keywords: Classification hiérarchique; Partitionnement non supervisé; Imagerie hyperspectrale; Végétation algale; Hierarchical classification; Unsupervised partitioning; Nonparametric; Hyperspectral imagery; Algal vegetation

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APA (6th Edition):

Chen, B. Y. (2016). Partitionnement non supervisé d'images hyperspectrales : application à l'identification de la végétation littorale : Unsupervised partitioning approach of hyperspectral image : application to the identification of the algal vegetation. (Doctoral Dissertation). Rennes 1. Retrieved from http://www.theses.fr/2016REN1S152

Chicago Manual of Style (16th Edition):

Chen, Bai Yang. “Partitionnement non supervisé d'images hyperspectrales : application à l'identification de la végétation littorale : Unsupervised partitioning approach of hyperspectral image : application to the identification of the algal vegetation.” 2016. Doctoral Dissertation, Rennes 1. Accessed January 16, 2021. http://www.theses.fr/2016REN1S152.

MLA Handbook (7th Edition):

Chen, Bai Yang. “Partitionnement non supervisé d'images hyperspectrales : application à l'identification de la végétation littorale : Unsupervised partitioning approach of hyperspectral image : application to the identification of the algal vegetation.” 2016. Web. 16 Jan 2021.

Vancouver:

Chen BY. Partitionnement non supervisé d'images hyperspectrales : application à l'identification de la végétation littorale : Unsupervised partitioning approach of hyperspectral image : application to the identification of the algal vegetation. [Internet] [Doctoral dissertation]. Rennes 1; 2016. [cited 2021 Jan 16]. Available from: http://www.theses.fr/2016REN1S152.

Council of Science Editors:

Chen BY. Partitionnement non supervisé d'images hyperspectrales : application à l'identification de la végétation littorale : Unsupervised partitioning approach of hyperspectral image : application to the identification of the algal vegetation. [Doctoral Dissertation]. Rennes 1; 2016. Available from: http://www.theses.fr/2016REN1S152

21. Valero Valbuena, Silvia. Arbre de partition binaire : un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales : Binary partition tree for hyperspectral imaging.

Degree: Docteur es, Signal, image, paroles, télécoms, 2011, Grenoble; Universitat politécnica de Catalunya

Résumé non communiqué par le doctorant.

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore,… (more)

Subjects/Keywords: Arbre binaire de partitions; Imagerie hyperspectrale; Filtrage; Segmentation; Teledetection; Partition tree; Hyperspectral imagery; Filtering; Segmentation; Remote sensing; 620

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APA (6th Edition):

Valero Valbuena, S. (2011). Arbre de partition binaire : un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales : Binary partition tree for hyperspectral imaging. (Doctoral Dissertation). Grenoble; Universitat politécnica de Catalunya. Retrieved from http://www.theses.fr/2011GRENT123

Chicago Manual of Style (16th Edition):

Valero Valbuena, Silvia. “Arbre de partition binaire : un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales : Binary partition tree for hyperspectral imaging.” 2011. Doctoral Dissertation, Grenoble; Universitat politécnica de Catalunya. Accessed January 16, 2021. http://www.theses.fr/2011GRENT123.

MLA Handbook (7th Edition):

Valero Valbuena, Silvia. “Arbre de partition binaire : un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales : Binary partition tree for hyperspectral imaging.” 2011. Web. 16 Jan 2021.

Vancouver:

Valero Valbuena S. Arbre de partition binaire : un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales : Binary partition tree for hyperspectral imaging. [Internet] [Doctoral dissertation]. Grenoble; Universitat politécnica de Catalunya; 2011. [cited 2021 Jan 16]. Available from: http://www.theses.fr/2011GRENT123.

Council of Science Editors:

Valero Valbuena S. Arbre de partition binaire : un nouvel outil pour la représentation hiérarchique et l’analyse des images hyperspectrales : Binary partition tree for hyperspectral imaging. [Doctoral Dissertation]. Grenoble; Universitat politécnica de Catalunya; 2011. Available from: http://www.theses.fr/2011GRENT123


Virginia Tech

22. Hayes, Austin Craig. Evaluating the potential of aerial remote sensing in flue-cured tobacco.

Degree: MS, Crop and Soil Environmental Sciences, 2019, Virginia Tech

 Unmanned Aerial Vehicle’s (UAVs) or drones, as they are commonly referred to, may have potential as a tool in flue-cured tobacco research and production. UAVs… (more)

Subjects/Keywords: remote sensing; flue-cured tobacco; unmanned aerial vehicle; UAV; ENDVI; vegetation index; nitrogen; black shank; Phytophthora nicotianae; hyperspectral imagery

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hayes, A. C. (2019). Evaluating the potential of aerial remote sensing in flue-cured tobacco. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90296

Chicago Manual of Style (16th Edition):

Hayes, Austin Craig. “Evaluating the potential of aerial remote sensing in flue-cured tobacco.” 2019. Masters Thesis, Virginia Tech. Accessed January 16, 2021. http://hdl.handle.net/10919/90296.

MLA Handbook (7th Edition):

Hayes, Austin Craig. “Evaluating the potential of aerial remote sensing in flue-cured tobacco.” 2019. Web. 16 Jan 2021.

Vancouver:

Hayes AC. Evaluating the potential of aerial remote sensing in flue-cured tobacco. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/10919/90296.

Council of Science Editors:

Hayes AC. Evaluating the potential of aerial remote sensing in flue-cured tobacco. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90296


University of Maryland

23. Broadwater, Joshua Bret. Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery.

Degree: Electrical Engineering, 2007, University of Maryland

Hyperspectral imagery provides the ability to detect targets that are smaller than the size of a pixel. They provide this ability by measuring the reflection… (more)

Subjects/Keywords: Engineering, Electronics and Electrical; hyperspectral; imagery; detection; subpixel

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APA (6th Edition):

Broadwater, J. B. (2007). Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/6820

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Broadwater, Joshua Bret. “Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery.” 2007. Thesis, University of Maryland. Accessed January 16, 2021. http://hdl.handle.net/1903/6820.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Broadwater, Joshua Bret. “Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery.” 2007. Web. 16 Jan 2021.

Vancouver:

Broadwater JB. Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery. [Internet] [Thesis]. University of Maryland; 2007. [cited 2021 Jan 16]. Available from: http://hdl.handle.net/1903/6820.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Broadwater JB. Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery. [Thesis]. University of Maryland; 2007. Available from: http://hdl.handle.net/1903/6820

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

24. Ruben, Pablo (author). 3D City Models in the Context of Urban Mining: A case study based on the CityGML model of Rotterdam.

Degree: 2019, Delft University of Technology

 Recently, the application of machine learning and data fusion techniques on hyperspectral imagery have demonstrated potential for ground cover classification at material level. Hereby, specific… (more)

Subjects/Keywords: enrichment; CityGML; hyperspectral imagery; urban mining; Rotterdam; APEX; spectral variation filtering; semantics; LOD; level of detail; point cloud; AHN

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APA (6th Edition):

Ruben, P. (. (2019). 3D City Models in the Context of Urban Mining: A case study based on the CityGML model of Rotterdam. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0fd2ffec-f25a-4391-be68-e816cc2611dd

Chicago Manual of Style (16th Edition):

Ruben, Pablo (author). “3D City Models in the Context of Urban Mining: A case study based on the CityGML model of Rotterdam.” 2019. Masters Thesis, Delft University of Technology. Accessed January 16, 2021. http://resolver.tudelft.nl/uuid:0fd2ffec-f25a-4391-be68-e816cc2611dd.

MLA Handbook (7th Edition):

Ruben, Pablo (author). “3D City Models in the Context of Urban Mining: A case study based on the CityGML model of Rotterdam.” 2019. Web. 16 Jan 2021.

Vancouver:

Ruben P(. 3D City Models in the Context of Urban Mining: A case study based on the CityGML model of Rotterdam. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 16]. Available from: http://resolver.tudelft.nl/uuid:0fd2ffec-f25a-4391-be68-e816cc2611dd.

Council of Science Editors:

Ruben P(. 3D City Models in the Context of Urban Mining: A case study based on the CityGML model of Rotterdam. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:0fd2ffec-f25a-4391-be68-e816cc2611dd


University of New South Wales

25. Dehaan, Remy L. Strategies for processing hyperspectral imagery in terrains of differing spectral and spatial variability.

Degree: Biological, Earth & Environmental Sciences, 2003, University of New South Wales

Subjects/Keywords: Image processing; Remote sensing; Digital techniques; Hyperspectral imagery

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APA (6th Edition):

Dehaan, R. L. (2003). Strategies for processing hyperspectral imagery in terrains of differing spectral and spatial variability. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/55521 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37932/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Dehaan, Remy L. “Strategies for processing hyperspectral imagery in terrains of differing spectral and spatial variability.” 2003. Doctoral Dissertation, University of New South Wales. Accessed January 16, 2021. http://handle.unsw.edu.au/1959.4/55521 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37932/SOURCE01?view=true.

MLA Handbook (7th Edition):

Dehaan, Remy L. “Strategies for processing hyperspectral imagery in terrains of differing spectral and spatial variability.” 2003. Web. 16 Jan 2021.

Vancouver:

Dehaan RL. Strategies for processing hyperspectral imagery in terrains of differing spectral and spatial variability. [Internet] [Doctoral dissertation]. University of New South Wales; 2003. [cited 2021 Jan 16]. Available from: http://handle.unsw.edu.au/1959.4/55521 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37932/SOURCE01?view=true.

Council of Science Editors:

Dehaan RL. Strategies for processing hyperspectral imagery in terrains of differing spectral and spatial variability. [Doctoral Dissertation]. University of New South Wales; 2003. Available from: http://handle.unsw.edu.au/1959.4/55521 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37932/SOURCE01?view=true


University of Dayton

26. Cusumano, Carl Joseph. Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance.

Degree: MS(M.S.), Electrical Engineering, 2019, University of Dayton

Hyperspectral imaging sensors suffer from pixel-to-pixel response nonuniformity that manifests as fixed pattern noise (FPN) in collected data. FPN is typically removed by application of… (more)

Subjects/Keywords: Electrical Engineering; Hyperspectral Imagery; Focal Plane Array; Residual Fixed Pattern Noise; Scene Based Nonuniformity Correction Algorithms; Receiver Operating Characteristic Curve

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APA (6th Edition):

Cusumano, C. J. (2019). Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565137429596905

Chicago Manual of Style (16th Edition):

Cusumano, Carl Joseph. “Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance.” 2019. Masters Thesis, University of Dayton. Accessed January 16, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565137429596905.

MLA Handbook (7th Edition):

Cusumano, Carl Joseph. “Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance.” 2019. Web. 16 Jan 2021.

Vancouver:

Cusumano CJ. Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance. [Internet] [Masters thesis]. University of Dayton; 2019. [cited 2021 Jan 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565137429596905.

Council of Science Editors:

Cusumano CJ. Assessment of Residual Nonuniformity on Hyperspectral Target Detection Performance. [Masters Thesis]. University of Dayton; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1565137429596905


Ohio University

27. Kaufman, Jason R. Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery.

Degree: PhD, Electrical Engineering (Engineering and Technology), 2014, Ohio University

 In recent years, hyperspectral imagery (HSI) has been increasingly used for mining, agricultural, and surveillance applications. Such imagery typically has fine spectral resolution but coarse… (more)

Subjects/Keywords: Remote Sensing; Electrical Engineering; hyperspectral imagery; high spatial resolution imagery; spatial-spectral feature extraction; image resolution enhancement; pansharpening; image fusion; material detection and identification

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APA (6th Edition):

Kaufman, J. R. (2014). Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery. (Doctoral Dissertation). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1408706595

Chicago Manual of Style (16th Edition):

Kaufman, Jason R. “Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery.” 2014. Doctoral Dissertation, Ohio University. Accessed January 16, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1408706595.

MLA Handbook (7th Edition):

Kaufman, Jason R. “Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery.” 2014. Web. 16 Jan 2021.

Vancouver:

Kaufman JR. Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery. [Internet] [Doctoral dissertation]. Ohio University; 2014. [cited 2021 Jan 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1408706595.

Council of Science Editors:

Kaufman JR. Spatial-Spectral Feature Extraction on Pansharpened Hyperspectral Imagery. [Doctoral Dissertation]. Ohio University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1408706595

28. Mobilia, Sean Joseph. Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks.

Degree: MS, Electrical Engineering, 2019, San Jose State University

Hyperspectral images are 3-D images, which contain data in hundreds of spectral bands as opposed to 2-D images, which contain data in at most… (more)

Subjects/Keywords: cancer detection; convolutional neural networks; data augmentation; hyperspectral imagery

…with hyperspectral satellite imagery, as there would be other available datasets to merge… …process hyperspectral imagery. One of the key aspects of hyperspectral classification is feature… …tools for hyperspectral imagery involve principal components analysis (PCA), support… …This chapter will provide a background of previous work done in hyperspectral imagery and… …diagnosis. Lu and Fei [10] have discussed the use of hyperspectral imagery and its role… 

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APA (6th Edition):

Mobilia, S. J. (2019). Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.7hhn-2p3k ; https://scholarworks.sjsu.edu/etd_theses/5010

Chicago Manual of Style (16th Edition):

Mobilia, Sean Joseph. “Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks.” 2019. Masters Thesis, San Jose State University. Accessed January 16, 2021. https://doi.org/10.31979/etd.7hhn-2p3k ; https://scholarworks.sjsu.edu/etd_theses/5010.

MLA Handbook (7th Edition):

Mobilia, Sean Joseph. “Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks.” 2019. Web. 16 Jan 2021.

Vancouver:

Mobilia SJ. Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2021 Jan 16]. Available from: https://doi.org/10.31979/etd.7hhn-2p3k ; https://scholarworks.sjsu.edu/etd_theses/5010.

Council of Science Editors:

Mobilia SJ. Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.7hhn-2p3k ; https://scholarworks.sjsu.edu/etd_theses/5010


Rochester Institute of Technology

29. Goodenough, Adam. Evaluating water quality monitoring with hyperspectral imagery.

Degree: 2001, Rochester Institute of Technology

 Water quality is an important indicator of the health of an environmental system. Traditionally, water quality analysis has involved directly sampling areas in question. This… (more)

Subjects/Keywords: Biological; Environmental system; Hydrologic; Hyperspectral imagery; Spectral character; Water quality

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Goodenough, A. (2001). Evaluating water quality monitoring with hyperspectral imagery. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/6787

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Goodenough, Adam. “Evaluating water quality monitoring with hyperspectral imagery.” 2001. Thesis, Rochester Institute of Technology. Accessed January 16, 2021. https://scholarworks.rit.edu/theses/6787.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Goodenough, Adam. “Evaluating water quality monitoring with hyperspectral imagery.” 2001. Web. 16 Jan 2021.

Vancouver:

Goodenough A. Evaluating water quality monitoring with hyperspectral imagery. [Internet] [Thesis]. Rochester Institute of Technology; 2001. [cited 2021 Jan 16]. Available from: https://scholarworks.rit.edu/theses/6787.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Goodenough A. Evaluating water quality monitoring with hyperspectral imagery. [Thesis]. Rochester Institute of Technology; 2001. Available from: https://scholarworks.rit.edu/theses/6787

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rochester Institute of Technology

30. Lee, Kyungsuk. A subpixel target detection algorithm for hyperspectral imagery.

Degree: Chester F. Carlson Center for Imaging Science (COS), 2003, Rochester Institute of Technology

 The goal of this research is to develop a new algorithm for the detection of subpixel scale target materials on the hyperspectral imagery. The signal… (more)

Subjects/Keywords: Atmospheric radiation; Dissertation; Hyperspectral imagery; Imaging science; Signal decision theory

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lee, K. (2003). A subpixel target detection algorithm for hyperspectral imagery. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/2998

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lee, Kyungsuk. “A subpixel target detection algorithm for hyperspectral imagery.” 2003. Thesis, Rochester Institute of Technology. Accessed January 16, 2021. https://scholarworks.rit.edu/theses/2998.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lee, Kyungsuk. “A subpixel target detection algorithm for hyperspectral imagery.” 2003. Web. 16 Jan 2021.

Vancouver:

Lee K. A subpixel target detection algorithm for hyperspectral imagery. [Internet] [Thesis]. Rochester Institute of Technology; 2003. [cited 2021 Jan 16]. Available from: https://scholarworks.rit.edu/theses/2998.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lee K. A subpixel target detection algorithm for hyperspectral imagery. [Thesis]. Rochester Institute of Technology; 2003. Available from: https://scholarworks.rit.edu/theses/2998

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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