Date of Award:
Master of Science (MS)
Electrical and Computer Engineering
Jacob H. Gunther
Removing the effects of the atmosphere from remote sensing data requires accurate knowledge of the physical properties of the atmosphere during the time of measurement. There is a nonlinear relationship that maps atmospheric composition to emitted spectra, but it cannot be easily inverted. The time evolution of atmospheric composition is approximately Markovian, and can be estimated using hyperspectral measurements of the atmosphere with particle filters. The difficulties associated with particle filtering high-dimension data can be mitigated by incorporating future measurement data with the proposal density.
Rawlings, Dustin, "Extracting Atmospheric Profiles from Hyperspectral Data Using Particle Filters" (2013). All Graduate Theses and Dissertations. 1533.
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .