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You searched for +publisher:"Rochester Institute of Technology" +contributor:("David Messinger"). Showing records 1 – 15 of 15 total matches.

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Rochester Institute of Technology

1. Yang, Jie. Crime Scene Blood Evidence Detection Using Spectral Imaging.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology

  Blood is the key evidence for forensic investigation because it carries critical information to help reconstruct the crime scene, confirm or exclude a suspect,… (more)

Subjects/Keywords: Bloodstain; Forensic; Hyperspectral; Imaging; Multispectral; Optical system

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APA (6th Edition):

Yang, J. (2019). Crime Scene Blood Evidence Detection Using Spectral Imaging. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10118

Chicago Manual of Style (16th Edition):

Yang, Jie. “Crime Scene Blood Evidence Detection Using Spectral Imaging.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/10118.

MLA Handbook (7th Edition):

Yang, Jie. “Crime Scene Blood Evidence Detection Using Spectral Imaging.” 2019. Web. 28 Feb 2021.

Vancouver:

Yang J. Crime Scene Blood Evidence Detection Using Spectral Imaging. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/10118.

Council of Science Editors:

Yang J. Crime Scene Blood Evidence Detection Using Spectral Imaging. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10118


Rochester Institute of Technology

2. Peery, Tyler R. System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology

  Cultural heritage imaging is becoming more common with the increased availability of more complex imaging systems, including multi- and hyperspectral imaging (MSI and HSI)… (more)

Subjects/Keywords: Cultural heritage; Hyperspectral imaging (HSI); Image quality; Low-light imaging; Panchromatic sharpening; Signal-to-noise ratio (SNR)

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APA (6th Edition):

Peery, T. R. (2019). System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10192

Chicago Manual of Style (16th Edition):

Peery, Tyler R. “System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/10192.

MLA Handbook (7th Edition):

Peery, Tyler R. “System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts.” 2019. Web. 28 Feb 2021.

Vancouver:

Peery TR. System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/10192.

Council of Science Editors:

Peery TR. System Design Considerations for a Low-Intensity Hyperspectral Imager of Sensitive Cultural Heritage Manuscripts. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10192


Rochester Institute of Technology

3. Bai, Di. A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology

  Hyperspectral image (HSI) classification has been used to identify material diversity in remote sensing images. Recently, hyperspectral imaging has been applied to historical artifact… (more)

Subjects/Keywords: 3D-SE-ResNet; Deep learning; Historical artifacts; Hyperspectral imaging; Image classification; Pigment mapping

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APA (6th Edition):

Bai, D. (2019). A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10264

Chicago Manual of Style (16th Edition):

Bai, Di. “A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/10264.

MLA Handbook (7th Edition):

Bai, Di. “A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods.” 2019. Web. 28 Feb 2021.

Vancouver:

Bai D. A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/10264.

Council of Science Editors:

Bai D. A Hyperspectral Image Classification Approach to Pigment Mapping of Historical Artifacts Using Deep Learning Methods. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10264


Rochester Institute of Technology

4. Hagstrom, Shea T. Voxel-Based LIDAR Analysis and Applications.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology

  One of the greatest recent changes in the field of remote sensing is the addition of high-quality Light Detection and Ranging (LIDAR) instruments. In… (more)

Subjects/Keywords: 3D; Analysis; Applications; LADAR; LIDAR; Voxel

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APA (6th Edition):

Hagstrom, S. T. (2014). Voxel-Based LIDAR Analysis and Applications. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8316

Chicago Manual of Style (16th Edition):

Hagstrom, Shea T. “Voxel-Based LIDAR Analysis and Applications.” 2014. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/8316.

MLA Handbook (7th Edition):

Hagstrom, Shea T. “Voxel-Based LIDAR Analysis and Applications.” 2014. Web. 28 Feb 2021.

Vancouver:

Hagstrom ST. Voxel-Based LIDAR Analysis and Applications. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/8316.

Council of Science Editors:

Hagstrom ST. Voxel-Based LIDAR Analysis and Applications. [Doctoral Dissertation]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8316


Rochester Institute of Technology

5. Kucer, Michal. Representations and representation learning for image aesthetics prediction and image enhancement.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2020, Rochester Institute of Technology

  With the continual improvement in cell phone cameras and improvements in the connectivity of mobile devices, we have seen an exponential increase in the… (more)

Subjects/Keywords: Image aesthetics; Image cropping; Image enhancement

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APA (6th Edition):

Kucer, M. (2020). Representations and representation learning for image aesthetics prediction and image enhancement. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10464

Chicago Manual of Style (16th Edition):

Kucer, Michal. “Representations and representation learning for image aesthetics prediction and image enhancement.” 2020. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/10464.

MLA Handbook (7th Edition):

Kucer, Michal. “Representations and representation learning for image aesthetics prediction and image enhancement.” 2020. Web. 28 Feb 2021.

Vancouver:

Kucer M. Representations and representation learning for image aesthetics prediction and image enhancement. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2020. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/10464.

Council of Science Editors:

Kucer M. Representations and representation learning for image aesthetics prediction and image enhancement. [Doctoral Dissertation]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10464

6. Fan, Lei. Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2016, Rochester Institute of Technology

  Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each… (more)

Subjects/Keywords: Data fusion; Graph theory; Lidar; Machine learning; Spatial-spectral; Tensor

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APA (6th Edition):

Fan, L. (2016). Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9396

Chicago Manual of Style (16th Edition):

Fan, Lei. “Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing.” 2016. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/9396.

MLA Handbook (7th Edition):

Fan, Lei. “Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing.” 2016. Web. 28 Feb 2021.

Vancouver:

Fan L. Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2016. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/9396.

Council of Science Editors:

Fan L. Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing. [Doctoral Dissertation]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9396

7. Sun, Weihua. Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2013, Rochester Institute of Technology

  Scene development plays the first step for synthetic image generation using DIRSIG (The Digital Imaging and Remote Sensing Image Generation Model). Traditionally the scenes… (more)

Subjects/Keywords: Feature extraction; Image fusion; Superresolution; Remote-sensing images – Data processing; Optical pattern recognition; Image analysis

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APA (6th Edition):

Sun, W. (2013). Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/956

Chicago Manual of Style (16th Edition):

Sun, Weihua. “Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images.” 2013. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/956.

MLA Handbook (7th Edition):

Sun, Weihua. “Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images.” 2013. Web. 28 Feb 2021.

Vancouver:

Sun W. Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2013. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/956.

Council of Science Editors:

Sun W. Knowledge-based Feature Extraction and Spectral Image Enhancement from Remotely Sensed Images. [Doctoral Dissertation]. Rochester Institute of Technology; 2013. Available from: https://scholarworks.rit.edu/theses/956

8. Ziemann, Amanda K. A manifold learning approach to target detection in high-resolution hyperspectral imagery.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2015, Rochester Institute of Technology

  Imagery collected from airborne platforms and satellites provide an important medium for remotely analyzing the content in a scene. In particular, the ability to… (more)

Subjects/Keywords: Graph theory; Hyperspectral; Manifold learning; Target detection

…component PCA Principal Components Analysis RGB red, green, and blue RIT Rochester Institute of… …Technology ROI region of interest RX Reed-Xiaoli Detector 14 LIST OF FIGURES SAM Spectral Angle… 

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APA (6th Edition):

Ziemann, A. K. (2015). A manifold learning approach to target detection in high-resolution hyperspectral imagery. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8617

Chicago Manual of Style (16th Edition):

Ziemann, Amanda K. “A manifold learning approach to target detection in high-resolution hyperspectral imagery.” 2015. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/8617.

MLA Handbook (7th Edition):

Ziemann, Amanda K. “A manifold learning approach to target detection in high-resolution hyperspectral imagery.” 2015. Web. 28 Feb 2021.

Vancouver:

Ziemann AK. A manifold learning approach to target detection in high-resolution hyperspectral imagery. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2015. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/8617.

Council of Science Editors:

Ziemann AK. A manifold learning approach to target detection in high-resolution hyperspectral imagery. [Doctoral Dissertation]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8617

9. Lewis, Christian M. The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations.

Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology

  Activity Based Intelligence (ABI) is the derivation of information from a series of in- dividual actions, interactions, and transactions being recorded over a period… (more)

Subjects/Keywords: Activity based intelligence; Activity recognition; Computer vision; Full motion video; Motion imagery; Multimodal (multispectral; polarimetric)

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APA (6th Edition):

Lewis, C. M. (2014). The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8324

Chicago Manual of Style (16th Edition):

Lewis, Christian M. “The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations.” 2014. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/8324.

MLA Handbook (7th Edition):

Lewis, Christian M. “The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations.” 2014. Web. 28 Feb 2021.

Vancouver:

Lewis CM. The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations. [Internet] [Masters thesis]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/8324.

Council of Science Editors:

Lewis CM. The Development of a Performance Assessment Methodology for Activity Based Intelligence: A Study of Spatial, Temporal, and Multimodal Considerations. [Masters Thesis]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8324

10. Sun, Jiangqin. Temporal Signature Modeling and Analysis.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology

  A vast amount of digital satellite and aerial images are collected over time, which calls for techniques to extract useful high-level information, such as… (more)

Subjects/Keywords: Clustering; Data mining; Image simulation; Parking lot model; Predictive models; Temporal modeling

…During the 2012 summer, RIT (Rochester Institute of Technology) performed a large… …Imaging and Remote Sensing Laboratory) at Rochester Institute of Technology has spent the… 

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APA (6th Edition):

Sun, J. (2014). Temporal Signature Modeling and Analysis. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8506

Chicago Manual of Style (16th Edition):

Sun, Jiangqin. “Temporal Signature Modeling and Analysis.” 2014. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/8506.

MLA Handbook (7th Edition):

Sun, Jiangqin. “Temporal Signature Modeling and Analysis.” 2014. Web. 28 Feb 2021.

Vancouver:

Sun J. Temporal Signature Modeling and Analysis. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/8506.

Council of Science Editors:

Sun J. Temporal Signature Modeling and Analysis. [Doctoral Dissertation]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8506

11. Stoddard, Jordyn. Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality.

Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology

  The adoption of cube-satellites (cubesats) by the space community has drastically lowered the cost of access to space and reduced the development lifecycle from… (more)

Subjects/Keywords: 3D reconstuction; Cubesats; Image quality; Point clouds; Structure from motion

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APA (6th Edition):

Stoddard, J. (2014). Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8308

Chicago Manual of Style (16th Edition):

Stoddard, Jordyn. “Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality.” 2014. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/8308.

MLA Handbook (7th Edition):

Stoddard, Jordyn. “Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality.” 2014. Web. 28 Feb 2021.

Vancouver:

Stoddard J. Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality. [Internet] [Masters thesis]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/8308.

Council of Science Editors:

Stoddard J. Toward Image-Based Three-Dimensional Reconstruction from Cubesats: Impacts of Spatial Resolution and SNR on Point Cloud Quality. [Masters Thesis]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8308

12. Albano, James A. Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2013, Rochester Institute of Technology

  Identification of materials from calibrated radiance data collected by an airborne imaging spectrometer depends strongly on the atmospheric and illumination conditions at the time… (more)

Subjects/Keywords: Remote sensing – Data processing; Spectrometer – Data processor; Multispectral photography

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APA (6th Edition):

Albano, J. A. (2013). Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/5951

Chicago Manual of Style (16th Edition):

Albano, James A. “Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique.” 2013. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/5951.

MLA Handbook (7th Edition):

Albano, James A. “Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique.” 2013. Web. 28 Feb 2021.

Vancouver:

Albano JA. Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2013. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/5951.

Council of Science Editors:

Albano JA. Spectral Target Detection using Physics-Based Modeling and a Manifold Learning Technique. [Doctoral Dissertation]. Rochester Institute of Technology; 2013. Available from: https://scholarworks.rit.edu/theses/5951

13. Harris, Michael L. Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features.

Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2014, Rochester Institute of Technology

  RIT's Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool allows modeling of real world scenes to create synthetic imagery for sensor design and… (more)

Subjects/Keywords: Filter; Gabor; Machine learning; Pattern recognition; Supervised classification

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APA (6th Edition):

Harris, M. L. (2014). Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8542

Chicago Manual of Style (16th Edition):

Harris, Michael L. “Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features.” 2014. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/8542.

MLA Handbook (7th Edition):

Harris, Michael L. “Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features.” 2014. Web. 28 Feb 2021.

Vancouver:

Harris ML. Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features. [Internet] [Masters thesis]. Rochester Institute of Technology; 2014. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/8542.

Council of Science Editors:

Harris ML. Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features. [Masters Thesis]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8542

14. Dorado-Munoz, Leidy P. Spectral Target Detecting Using Schroedinger Eigenmaps.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2016, Rochester Institute of Technology

  Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific… (more)

Subjects/Keywords: Image/data processing; Machine learning; Multispectral imaging; Target/object detection

…the electromagnetic spectrum RIT Rochester Institute of Technology ROC Receiver Operating… 

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APA (6th Edition):

Dorado-Munoz, L. P. (2016). Spectral Target Detecting Using Schroedinger Eigenmaps. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9186

Chicago Manual of Style (16th Edition):

Dorado-Munoz, Leidy P. “Spectral Target Detecting Using Schroedinger Eigenmaps.” 2016. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/9186.

MLA Handbook (7th Edition):

Dorado-Munoz, Leidy P. “Spectral Target Detecting Using Schroedinger Eigenmaps.” 2016. Web. 28 Feb 2021.

Vancouver:

Dorado-Munoz LP. Spectral Target Detecting Using Schroedinger Eigenmaps. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2016. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/9186.

Council of Science Editors:

Dorado-Munoz LP. Spectral Target Detecting Using Schroedinger Eigenmaps. [Doctoral Dissertation]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9186


Rochester Institute of Technology

15. Kwong, Justin. Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation.

Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2009, Rochester Institute of Technology

  This project is an application of remote sensing techniques to the field of archaeology. Clustering and unmixing algorithms are applied to hyperspectral Hyperion imagery… (more)

Subjects/Keywords: Gradient flow; Hyperion; Hyperspectral; MaxD; Stepwise unmixing; Zapotec

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APA (6th Edition):

Kwong, J. (2009). Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9083

Chicago Manual of Style (16th Edition):

Kwong, Justin. “Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation.” 2009. Masters Thesis, Rochester Institute of Technology. Accessed February 28, 2021. https://scholarworks.rit.edu/theses/9083.

MLA Handbook (7th Edition):

Kwong, Justin. “Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation.” 2009. Web. 28 Feb 2021.

Vancouver:

Kwong J. Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation. [Internet] [Masters thesis]. Rochester Institute of Technology; 2009. [cited 2021 Feb 28]. Available from: https://scholarworks.rit.edu/theses/9083.

Council of Science Editors:

Kwong J. Hyperspectral Clustering and Unmixing of Satellite Imagery for the Study of Complex Society State Formation. [Masters Thesis]. Rochester Institute of Technology; 2009. Available from: https://scholarworks.rit.edu/theses/9083

.