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Unsupervised unmixing of hyperspectr...

Masalmah, Yahya M.

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Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization./
Author:	Masalmah, Yahya M.
Description:	160 p.
Notes:	Adviser: Miguel Velez-Reyes.
Contained By:	Dissertation Abstracts International69-01B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3296052
ISBN:	9780549408567

Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization.
Masalmah, Yahya M.

Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization. - 160 p.

Adviser: Miguel Velez-Reyes.

Thesis (Ph.D.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2008.

In hyperspectral imaging, hundreds of images are taken at narrow and contiguous spectral bands providing us with high spectral resolution spectral signatures that can be used to discriminate between objects. In many applications, the measured spectral signature is a mixture of the object of interest, and other objects within the field of view of the sensor. To determine which objects are in the field of view of the sensor, we need to decompose the measured spectral signature in its constituents and their contribution to the measured signal. This research dealt with the unsupervised determination of the constituents and their fractional abundance in each pixel in a hyperspectral image using a constrained positive matrix factorization (cPMF). Different algorithms are presented to compute the cPMF. Tests and validation with real and simulated data show the effectiveness of the method. Application of the approach to environmental remote sensing and microscopic imaging is shown.

ISBN: 9780549408567Subjects--Topical Terms:

626642
Computer Science.

Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization.
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020 $a 9780549408567
035 $a (UMI)AAI3296052
035 $a AAI3296052
040 $a UMI $c UMI
100 1 $a Masalmah, Yahya M. $3 1264063
245 1 0 $a Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization.
300 $a 160 p.
500 $a Adviser: Miguel Velez-Reyes.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0427.
502 $a Thesis (Ph.D.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2008.
520 $a In hyperspectral imaging, hundreds of images are taken at narrow and contiguous spectral bands providing us with high spectral resolution spectral signatures that can be used to discriminate between objects. In many applications, the measured spectral signature is a mixture of the object of interest, and other objects within the field of view of the sensor. To determine which objects are in the field of view of the sensor, we need to decompose the measured spectral signature in its constituents and their contribution to the measured signal. This research dealt with the unsupervised determination of the constituents and their fractional abundance in each pixel in a hyperspectral image using a constrained positive matrix factorization (cPMF). Different algorithms are presented to compute the cPMF. Tests and validation with real and simulated data show the effectiveness of the method. Application of the approach to environmental remote sensing and microscopic imaging is shown.
590 $a School code: 0553.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Remote Sensing. $3 1018559
690 $a 0775
690 $a 0799
690 $a 0984
710 2 $a University of Puerto Rico, Mayaguez (Puerto Rico). $3 1017811
773 0 $t Dissertation Abstracts International $g 69-01B.
790 $a 0553
790 1 0 $a Velez-Reyes, Miguel, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3296052