An Investigation Into Hyperspectral Imagery Generation
Date of Award
12-12-2024
Degree Name
M.S. in Computer Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Bradley Ratliff
Abstract
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.
Keywords
Hyperspectral Imagery, Remote Sensing, Deep Learning, Generative Adversarial Networks, Ground Truth Image Generation, Image Generation
Rights Statement
Copyright © 2024, author.
Recommended Citation
Gaydosh, Theodore, "An Investigation Into Hyperspectral Imagery Generation" (2024). Graduate Theses and Dissertations. 7472.
https://ecommons.udayton.edu/graduate_theses/7472
