Remotely sensed hyperspectral image unmixing.

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dc.contributor.advisor Farison, James Blair. Yang, Zhuocheng.
dc.contributor.other Baylor University. Dept. of Electrical and Computer Engineering. en 2010-08
dc.description.abstract Estimating 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.rights Baylor 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 for inquiries about permission. en
dc.subject Hyperspectral imaging. en
dc.subject Linear unmixing. en
dc.subject Parameter estimation. en
dc.subject Quadratic programming. en
dc.subject Remote sensing. en
dc.title Remotely sensed hyperspectral image unmixing. en
dc.type Thesis en M.S.E.C.E. en
dc.rights.accessrights Worldwide access en
dc.contributor.department Engineering. en

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