Combined Spatial-Spectral Processing of Multisource Data Using Thematic Content

Persistent Link:
http://hdl.handle.net/10150/195787
Title:
Combined Spatial-Spectral Processing of Multisource Data Using Thematic Content
Author:
Filiberti, Daniel Paul
Issue Date:
2005
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:
In this dissertation, I design a processing approach, implement and test several solutions to combining spatial and spectral processing of multisource data. The measured spectral information is assumed to come from a multispectral or hyperspectral imaging system with low spatial resolution. Thematic content from a higher spatial resolution sensor is used to spatially localize different materials by their spectral signature. This approach results in both spectralunmixing and sharpening, a spatial-spectral fusion. The main real imagery example, fusion of polarimetric synthetic aperture radar (SAR) with hyperspectral imagery, poses a unique challenge due to the phenomenological differences between the sensors.Theoretical models for electro-optical image formation and scene reflectivity are shown to lead naturally to a set of pixel mixing equations. Several solutions for the spatial unmixing form of these equations are examined, based on the method of least squares. In particular, a method for introducing thematic content into the solution for spatial unmixing is defined using weighted least squares. Finally, and most significantly, a spatial-spectral fusion algorithm based on the theory of projection onto convex sets (POCS) is presented. Theoretical aspects of POCS are briefly discussed, showing how the use of constraints in the form of closed convex sets drives the solution. Then, constraints are derived that are intimately tied to the underlying theoretical models. Simulated imagery is used to characterize the different constraintcombinations that can be used in a POCS-based fusion algorithm.The fusion algorithms are applied to real imagery from two data sets, a Landsat ETM+ scene over Tucson, AZ and an AVIRIS/AirSAR scene over Tombstone, AZ. The results of the fusion are analyzed using scattergrams and correlation statistics. The POCS-based fusion algorithm is shown to produce a reasonable fusion of the AVIRIS/AirSAR data, with some sharpening of spatial-spectral features.
Type:
text; Electronic Dissertation
Keywords:
spatial-spectral fusion; sharpening; unmixing; hyperspectral; synthetic aperture radar; multisource
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Electrical & Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Schowengerdt, Robert A.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleCombined Spatial-Spectral Processing of Multisource Data Using Thematic Contenten_US
dc.creatorFiliberti, Daniel Paulen_US
dc.contributor.authorFiliberti, Daniel Paulen_US
dc.date.issued2005en_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.description.abstractIn this dissertation, I design a processing approach, implement and test several solutions to combining spatial and spectral processing of multisource data. The measured spectral information is assumed to come from a multispectral or hyperspectral imaging system with low spatial resolution. Thematic content from a higher spatial resolution sensor is used to spatially localize different materials by their spectral signature. This approach results in both spectralunmixing and sharpening, a spatial-spectral fusion. The main real imagery example, fusion of polarimetric synthetic aperture radar (SAR) with hyperspectral imagery, poses a unique challenge due to the phenomenological differences between the sensors.Theoretical models for electro-optical image formation and scene reflectivity are shown to lead naturally to a set of pixel mixing equations. Several solutions for the spatial unmixing form of these equations are examined, based on the method of least squares. In particular, a method for introducing thematic content into the solution for spatial unmixing is defined using weighted least squares. Finally, and most significantly, a spatial-spectral fusion algorithm based on the theory of projection onto convex sets (POCS) is presented. Theoretical aspects of POCS are briefly discussed, showing how the use of constraints in the form of closed convex sets drives the solution. Then, constraints are derived that are intimately tied to the underlying theoretical models. Simulated imagery is used to characterize the different constraintcombinations that can be used in a POCS-based fusion algorithm.The fusion algorithms are applied to real imagery from two data sets, a Landsat ETM+ scene over Tucson, AZ and an AVIRIS/AirSAR scene over Tombstone, AZ. The results of the fusion are analyzed using scattergrams and correlation statistics. The POCS-based fusion algorithm is shown to produce a reasonable fusion of the AVIRIS/AirSAR data, with some sharpening of spatial-spectral features.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectspatial-spectral fusionen_US
dc.subjectsharpeningen_US
dc.subjectunmixingen_US
dc.subjecthyperspectralen_US
dc.subjectsynthetic aperture radaren_US
dc.subjectmultisourceen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairSchowengerdt, Robert A.en_US
dc.contributor.committeememberMarcellin, Michael W.en_US
dc.contributor.committeememberStrickland, Robin N.en_US
dc.contributor.committeememberThome, Kurtis J.en_US
dc.contributor.committeememberHuete, Alfredo R.en_US
dc.identifier.proquest1066en_US
dc.identifier.oclc137353791en_US
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