Calibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imaging

Persistent Link:
http://hdl.handle.net/10150/579668
Title:
Calibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imaging
Author:
Poon, Phillip; Dunlop, Matthew
Advisor:
Gehm, Michael
Affiliation:
University of Arizona
Issue Date:
2013-10
Rights:
Copyright © held by the author; distribution rights International Foundation for Telemetering
Collection Information:
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.
Publisher:
International Foundation for Telemetering
Journal:
International Telemetering Conference Proceedings
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.
Keywords:
Compressive Sensing; hyperspectral imaging; calibration
Sponsors:
International Foundation for Telemetering
ISSN:
0884-5123; 0074-9079
Additional Links:
http://www.telemetry.org/

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleCalibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imagingen_US
dc.contributor.authorPoon, Phillipen
dc.contributor.authorDunlop, Matthewen
dc.contributor.advisorGehm, Michaelen_US
dc.contributor.departmentUniversity of Arizonaen
dc.date.issued2013-10en
dc.rightsCopyright © held by the author; distribution rights International Foundation for Telemeteringen_US
dc.description.collectioninformationProceedings 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
dc.publisherInternational Foundation for Telemeteringen
dc.description.abstractCompressive 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.en
dc.subjectCompressive Sensingen
dc.subjecthyperspectral imagingen
dc.subjectcalibrationen
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.identifier.issn0884-5123en
dc.identifier.issn0074-9079en
dc.identifier.urihttp://hdl.handle.net/10150/579668en
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.typetexten
dc.typeProceedingsen
dc.relation.urlhttp://www.telemetry.org/en
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