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dc.contributor.authorWu, Hsien-Huang.
dc.creatorWu, Hsien-Huang.en_US
dc.date.accessioned2011-10-31T17:58:55Z
dc.date.available2011-10-31T17:58:55Z
dc.date.issued1992en_US
dc.identifier.urihttp://hdl.handle.net/10150/186114
dc.description.abstractBecause of the resolution limitations in remote sensing, the radiance recorded by the detector at each pixel is the integrated sum of the spectral radiance of all materials within the detector instantaneous-field-of-view (IFOV). If the detector IFOV covers more than one object class, the radiance detected is not characteristic of any single class but a mixture of all classes. These mixed pixels will have spectral signatures that fall within the convex hull formed by the signatures of all the classes. Traditional classifiers are therefore usually left with many misclassified or unclassified pixels. To remedy this problem, unmixing algorithms which decompose each pixel into a combination of several classes have been successfully applied to estimate the percentage of each class inside one pixel. In this dissertation, unmixing error of the least squares unmixing algorithm that is caused by the intrinsic data variance, system PSF blurring, detection noise, and band-to-band misregistration is analyzed and evaluated. For high unmixing accuracy, image restoration is proposed to remove the PSF blurring degradation. To objectively assess the restoration performance and expedite the design of our application-oriented restoration scheme, and objective criterion based on the measurement of spectral fidelity in frequency domain is suggested. Based on this criterion, a detailed comparison between the conventional Wiener filter and sampled Wiener filter is conducted, which highlights the significance of sampling aliasing and verifies the results obtained visually by other researchers. Our study shows that contrary to restoration for visual purposes, a partial restoration scheme, instead of full restoration, should be used for a better unmixing performance. Also, the sampling aliasing, which is an artifact and should be suppressed in traditional restoration application, is actually a signal component which needs to be restored for unmixing. Under fair SNR conditions ($\ge$30dB), the proposed restoration scheme can reduce the total unmixing error up to 40% to 70% depending on the scene complexity.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
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_US
dc.subjectEngineering.en_US
dc.titleImage restoration for improved spectral unmixing.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.contributor.chairSchowengerdt, Robert A.en_US
dc.identifier.oclc701363275en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberStrickland, Robin N.en_US
dc.contributor.committeememberRyan, Thomas W.en_US
dc.contributor.committeememberDowney, Peter A.en_US
dc.identifier.proquest9313014en_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
dc.description.noteThis item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu.
dc.description.admin-noteOriginal file replaced with corrected file September 2023.
refterms.dateFOA2018-08-23T10:05:31Z
html.description.abstractBecause of the resolution limitations in remote sensing, the radiance recorded by the detector at each pixel is the integrated sum of the spectral radiance of all materials within the detector instantaneous-field-of-view (IFOV). If the detector IFOV covers more than one object class, the radiance detected is not characteristic of any single class but a mixture of all classes. These mixed pixels will have spectral signatures that fall within the convex hull formed by the signatures of all the classes. Traditional classifiers are therefore usually left with many misclassified or unclassified pixels. To remedy this problem, unmixing algorithms which decompose each pixel into a combination of several classes have been successfully applied to estimate the percentage of each class inside one pixel. In this dissertation, unmixing error of the least squares unmixing algorithm that is caused by the intrinsic data variance, system PSF blurring, detection noise, and band-to-band misregistration is analyzed and evaluated. For high unmixing accuracy, image restoration is proposed to remove the PSF blurring degradation. To objectively assess the restoration performance and expedite the design of our application-oriented restoration scheme, and objective criterion based on the measurement of spectral fidelity in frequency domain is suggested. Based on this criterion, a detailed comparison between the conventional Wiener filter and sampled Wiener filter is conducted, which highlights the significance of sampling aliasing and verifies the results obtained visually by other researchers. Our study shows that contrary to restoration for visual purposes, a partial restoration scheme, instead of full restoration, should be used for a better unmixing performance. Also, the sampling aliasing, which is an artifact and should be suppressed in traditional restoration application, is actually a signal component which needs to be restored for unmixing. Under fair SNR conditions ($\ge$30dB), the proposed restoration scheme can reduce the total unmixing error up to 40% to 70% depending on the scene complexity.


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