Calibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imaging
dc.contributor.advisor | Gehm, Michael | en_US |
dc.contributor.author | Poon, Phillip | |
dc.contributor.author | Dunlop, Matthew | |
dc.date.accessioned | 2015-10-14T16:46:08Z | en |
dc.date.available | 2015-10-14T16:46:08Z | en |
dc.date.issued | 2013-10 | en |
dc.identifier.issn | 0884-5123 | en |
dc.identifier.issn | 0074-9079 | en |
dc.identifier.uri | http://hdl.handle.net/10150/579668 | en |
dc.description | ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV | en_US |
dc.description.abstract | Compressive Sensing (CS) is a set of techniques that can faithfully acquire a signal from sub- Nyquist measurements, provided the class of signals have certain broadly-applicable properties. Reconstruction (or exploitation) of the signal from these sub-Nyquist measurements requires a forward model - knowledge of how the system maps signals to measurements. In high-dimensional CS systems, determination of this forward model via direct measurement of the system response to the complete set of impulse functions is impractical. In this paper, we will discuss the development of a parameterized forward model for the Adaptive, Feature-Specific Spectral Imaging Classifier (AFSSI-C), an experimental compressive spectral image classifier. This parameterized forward model drastically reduces the number of calibration measurements. | |
dc.description.sponsorship | International Foundation for Telemetering | en |
dc.language.iso | en_US | en |
dc.publisher | International Foundation for Telemetering | en |
dc.relation.url | http://www.telemetry.org/ | en |
dc.rights | Copyright © held by the author; distribution rights International Foundation for Telemetering | en_US |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | Compressive Sensing | en |
dc.subject | hyperspectral imaging | en |
dc.subject | calibration | en |
dc.title | Calibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imaging | en_US |
dc.type | text | en |
dc.type | Proceedings | en |
dc.contributor.department | University of Arizona | en |
dc.identifier.journal | International Telemetering Conference Proceedings | en |
dc.description.collectioninformation | Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection. | en_US |
refterms.dateFOA | 2018-09-10T14:51:50Z | |
html.description.abstract | Compressive Sensing (CS) is a set of techniques that can faithfully acquire a signal from sub- Nyquist measurements, provided the class of signals have certain broadly-applicable properties. Reconstruction (or exploitation) of the signal from these sub-Nyquist measurements requires a forward model - knowledge of how the system maps signals to measurements. In high-dimensional CS systems, determination of this forward model via direct measurement of the system response to the complete set of impulse functions is impractical. In this paper, we will discuss the development of a parameterized forward model for the Adaptive, Feature-Specific Spectral Imaging Classifier (AFSSI-C), an experimental compressive spectral image classifier. This parameterized forward model drastically reduces the number of calibration measurements. |