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Gaydosh final thesis__final format approved LW 12-9-24.pdf (2.24 MB)

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ETD Abstract Container

Abstract Header

An Investigation Into Hyperspectral Imagery Generation

Gaydosh, Theodore J
http://orcid.org/0009-0006-5811-7094
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1733950652268343

Abstract Details

2024, Master of Science in Computer Engineering, University of Dayton, Electrical and Computer Engineering.
The lack of Hyperspectral imagery (HSI) is an issue for many researchers and fields that wish to utilize the sheer amount of data a HSI cube contains. Given this along with the cost and the effort associated with gathering HSI, a way to generate them using existing would be very useful. Other works have generated synthetic images, images that contain the characteristics of a HSI cube, but that do not actually map to any real world location. This work attempts to show that it is possible to generate those cubes with easier to gather datasets and less data. This is done by using a paired image generation deep learning model, a Generative Adversarial Network. The HSI cubes were gathered from USGS’s Earth Explorer and the sensor used was Earth Observing-1’s Hyperion. The network was trained on four different input types in four regions and tested on three different regions. The four input types were 5 bands, 10 bands, 10 bands with no bands from the middle 100 bands, and 20 bands. The results and accuracy of the model were based on various metrics and a separate model was trained on each input until those metrics plateaued. A comparison of input vs generated spectra as well as the various metrics were then used to verify the accuracy of the test dataset. It was found the models each generalized well and that even individual bands of the greater HSI cube generated quite well to the target.
Bradley Ratliff (Advisor)
Theus Aspiras (Committee Member)
Eric Balster (Committee Member)
35 p.
Computer Engineering; Computer Science; Remote Sensing
Hyperspectral Imagery; Remote Sensing; Deep Learning; Generative Adversarial Networks; Ground Truth Image Generation; Image Generation

Recommended Citations

Citations

  • Gaydosh, T. J. (2024). An Investigation Into Hyperspectral Imagery Generation [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1733950652268343

    APA Style (7th edition)

  • Gaydosh, Theodore. An Investigation Into Hyperspectral Imagery Generation. 2024. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1733950652268343.

    MLA Style (8th edition)

  • Gaydosh, Theodore. "An Investigation Into Hyperspectral Imagery Generation." Master's thesis, University of Dayton, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1733950652268343

    Chicago Manual of Style (17th edition)

dayton1733950652268343
35
© 2024, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.
Release 3.2.12