Remotely sensed hyperspectral image unmixing.

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dc.contributor.advisorFarison, James Blair.
dc.contributor.authorYang, Zhuocheng.
dc.contributor.departmentEngineering.en
dc.contributor.otherBaylor University. Dept. of Electrical and Computer Engineering.en
dc.date.accessioned2010-10-08T16:34:43Z
dc.date.available2010-10-08T16:34:43Z
dc.date.copyright2010-08
dc.date.issued2010-10-08T16:34:43Z
dc.descriptionIncludes bibliographical references (p. ).en
dc.description.abstractEstimating abundance fractions of materials in hyperspectral images is an important area of study in the field of remote sensing. The need for liner unmixing in remotely sensed imagery arises from the fact that the sampling distance is generally larger than the size of the targets of interest. We present two new unmixing methods, both of which are based on a linear mixture model. The first method requires two physical constraints imposed on abundance fractions: the abundance sum-to-one constraint and the abundance nonnegativity constraint. The second method relaxes the abundance sum-to-one constraint as this condition is rarely satisfied in reality and uses the relaxed sum-to-one constraint instead. Another contribution of this work is that the estimation is, unlike many other proposed methods, performed on noise reduced hyperspectral images instead of original images.en
dc.description.degreeM.S.E.C.E.en
dc.description.statementofresponsibilityby Zhuocheng Yang.en
dc.format.extent3109938 bytes
dc.format.extent4219795 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2104/8068
dc.language.isoen_USen
dc.rightsBaylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission.en
dc.rights.accessrightsWorldwide accessen
dc.subjectHyperspectral imaging.en
dc.subjectLinear unmixing.en
dc.subjectParameter estimation.en
dc.subjectQuadratic programming.en
dc.subjectRemote sensing.en
dc.titleRemotely sensed hyperspectral image unmixing.en
dc.typeThesisen

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