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    Sparse Representations of Hyperspectral Images

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    Type
    Thesis
    Authors
    Swanson, Robin J. cc
    Advisors
    Heidrich, Wolfgang cc
    Committee members
    Hadwiger, Markus cc
    Ghanem, Bader cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2015-11-23
    Permanent link to this record
    http://hdl.handle.net/10754/583304
    
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    Abstract
    Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.
    Citation
    Swanson, R. J. (2015). Sparse Representations of Hyperspectral Images. KAUST Research Repository. https://doi.org/10.25781/KAUST-E7C30
    DOI
    10.25781/KAUST-E7C30
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-E7C30
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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