Seeing Beyond Sight: The Adaptive, Feature-Specific, Spectral Imaging Classifier

Persistent Link:
http://hdl.handle.net/10150/566240
Title:
Seeing Beyond Sight: The Adaptive, Feature-Specific, Spectral Imaging Classifier
Author:
Dunlop-Gray, Matthew John
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Spectral imaging, a combination of spectroscopy and imaging, is a powerful tool for providing in situ material classification across a spatial scene. Typically spectral imaging analyses are interested in classification, though conventionally the classification is performed only after reconstruction of the spectral datacube, which can have upwards of 10⁹ signal elements. In this dissertation, I present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator which induces spectral filtering, the AFSSI-C measures specific projections of the spectral datacube which in turn feed an adaptive Bayesian classification and feature design framework. I present my work related to the design, construction, and testing of this instrument, which ultimately demonstrated significantly improved classification accuracy compared to legacy spectral imaging systems by first showing agreement with simulation, and then comparing to expected performance of traditional systems. As a result of its open aperture and adaptive filters, the AFSSI-C achieves 250 X better accuracy than pushbroom, whiskbroom, and tunable filter systems for a four-class problem at 0 dB TSNR (task signal-to-noise ratio) - a point where measurement noise is equal to the minimum separation between the library spectra. The AFSSI-C also achieves 100 X better accuracy than random projections at 0 dB TSNR.
Type:
text; Electronic Dissertation
Keywords:
Feature-based Measurement; Spectral Classification; Spectral Imaging; Electrical & Computer Engineering; Adaptive
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical & Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Gehm, Michael; Ashok, Amit

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleSeeing Beyond Sight: The Adaptive, Feature-Specific, Spectral Imaging Classifieren_US
dc.creatorDunlop-Gray, Matthew Johnen
dc.contributor.authorDunlop-Gray, Matthew Johnen
dc.date.issued2015en
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.description.abstractSpectral imaging, a combination of spectroscopy and imaging, is a powerful tool for providing in situ material classification across a spatial scene. Typically spectral imaging analyses are interested in classification, though conventionally the classification is performed only after reconstruction of the spectral datacube, which can have upwards of 10⁹ signal elements. In this dissertation, I present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator which induces spectral filtering, the AFSSI-C measures specific projections of the spectral datacube which in turn feed an adaptive Bayesian classification and feature design framework. I present my work related to the design, construction, and testing of this instrument, which ultimately demonstrated significantly improved classification accuracy compared to legacy spectral imaging systems by first showing agreement with simulation, and then comparing to expected performance of traditional systems. As a result of its open aperture and adaptive filters, the AFSSI-C achieves 250 X better accuracy than pushbroom, whiskbroom, and tunable filter systems for a four-class problem at 0 dB TSNR (task signal-to-noise ratio) - a point where measurement noise is equal to the minimum separation between the library spectra. The AFSSI-C also achieves 100 X better accuracy than random projections at 0 dB TSNR.en
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectFeature-based Measurementen
dc.subjectSpectral Classificationen
dc.subjectSpectral Imagingen
dc.subjectElectrical & Computer Engineeringen
dc.subjectAdaptiveen
thesis.degree.namePh.D.en
thesis.degree.leveldoctoralen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineElectrical & Computer Engineeringen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorGehm, Michaelen
dc.contributor.advisorAshok, Amiten
dc.contributor.committeememberGehm, Michaelen
dc.contributor.committeememberAshok, Amiten
dc.contributor.committeememberXin, Haoen
dc.contributor.committeememberZiolkowski, Richarden
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.