Type
ThesisAuthors
Swanson, Robin J.
Advisors
Heidrich, Wolfgang
Committee members
Hadwiger, Markus
Ghanem, Bader

Program
Computer ScienceDate
2015-11-23Permanent link to this record
http://hdl.handle.net/10754/583304
Metadata
Show full item recordAbstract
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.ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-E7C30