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Hyperspectal W-Net: Exploratory Unsupervised Hyperspectral Image Segmentation

Steiner, Adam J
http://orcid.org/0009-0005-2756-5938
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714480306694923

Abstract Details

2024, Master of Science in Electrical Engineering, University of Dayton, Electrical Engineering.
Remote sensing techniques are capable of capturing large scenes of data over several sensing domains. Hyperspectral imagery (HSI), often accompanied with lIDAR and orthoimagery sensors during collection, can provide deeper contextual information for a wide range of applications in many different fields. Complex characteristics across spectral bands in addition to high-dimensionality of HSI data present challenges to accurate classification. Generally, dimensionality reduction of the input hyperspectral data cube is performed through multi-phase analytical algorithms as a pre-processing step before further analysis to include machine learning networks. These networks commonly rely on labeled training data for segmentation. Annotating ground truth aerial data can prove to be a cumbersome endeavor that may require specific expertise for accurate assessment. This inspires exploratory research for useful unsupervised feature-learning approaches that can withdraw essential information from HSI data to map scenes without labeled data thereby providing a start-to-finish scene segmentation process.
Vijayan Asari (Committee Chair)
Theus Aspiras (Advisor)
Brad Ratliff (Advisor)
Electrical Engineering; Engineering; Environmental Geology; Environmental Science; Environmental Studies; Geology; Geophysics; Remote Sensing; Urban Planning
Hyperspectral, U-Net, W-Net, Autoencoder, latent space, latent representation, classification, segmentation, convolutional neural network, CNN, remote sensing,

Recommended Citations

Citations

  • Steiner, A. J. (2024). Hyperspectal W-Net: Exploratory Unsupervised Hyperspectral Image Segmentation [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714480306694923

    APA Style (7th edition)

  • Steiner, Adam. Hyperspectal W-Net: Exploratory Unsupervised Hyperspectral Image Segmentation. 2024. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714480306694923.

    MLA Style (8th edition)

  • Steiner, Adam. "Hyperspectal W-Net: Exploratory Unsupervised Hyperspectral Image Segmentation." Master's thesis, University of Dayton, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1714480306694923

    Chicago Manual of Style (17th edition)

dayton1714480306694923
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© 2024, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.