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Iterative algorithms for abundance e...

Rosario Torres, Samuel.

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Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery./
Author:	Rosario Torres, Samuel.
Description:	79 p.
Notes:	Source: Masters Abstracts International, Volume: 43-01, page: 0284.
Contained By:	Masters Abstracts International43-01.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1421840
ISBN:	0496265857

Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery.
Rosario Torres, Samuel.

Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery. - 79 p.

Source: Masters Abstracts International, Volume: 43-01, page: 0284.

Thesis (M.S.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2004.

Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data organized in the so-called hyperspectral cube. The spatial resolution of most Hyperspectral Imagery (HSI) sensors flown nowadays is larger than the size of the objects being observed. Therefore, the measured spectral signature is a mixture of the signatures of the objects in the field of view of the sensor. The high spectral resolution can be used to decompose the measured spectra into its constituents. This is the so-called unmixing problem in HSI. Spectral unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra, or endmembers, and a set of corresponding fractions or abundances. Unmixing allows us to detect and classify subpixel objects by their contribution to the measured spectral signal. In this research, two new abundance estimation algorithms based on a least distance least square problem and compare it with other approaches presented in the literature were developed. Algorithm validation and comparison are done with real and simulated HSI data. HSI Abundance Estimation Toolbox ( HABET) was implemented in the ENVI/IDL environment. Application of the unmixing algorithm for remote sensing of benthic habitats is presented.

ISBN: 0496265857Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery.
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100 1 $a Rosario Torres, Samuel. $3 1939978
245 1 0 $a Iterative algorithms for abundance estimation on unmixing of hyperspectral imagery.
300 $a 79 p.
500 $a Source: Masters Abstracts International, Volume: 43-01, page: 0284.
500 $a Adviser: Miguel Velez Reyes.
502 $a Thesis (M.S.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2004.
520 $a Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data organized in the so-called hyperspectral cube. The spatial resolution of most Hyperspectral Imagery (HSI) sensors flown nowadays is larger than the size of the objects being observed. Therefore, the measured spectral signature is a mixture of the signatures of the objects in the field of view of the sensor. The high spectral resolution can be used to decompose the measured spectra into its constituents. This is the so-called unmixing problem in HSI. Spectral unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra, or endmembers, and a set of corresponding fractions or abundances. Unmixing allows us to detect and classify subpixel objects by their contribution to the measured spectral signal. In this research, two new abundance estimation algorithms based on a least distance least square problem and compare it with other approaches presented in the literature were developed. Algorithm validation and comparison are done with real and simulated HSI data. HSI Abundance Estimation Toolbox ( HABET) was implemented in the ENVI/IDL environment. Application of the unmixing algorithm for remote sensing of benthic habitats is presented.
590 $a School code: 0553.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Computer Science. $3 626642
650 4 $a Remote Sensing. $3 1018559
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710 2 0 $a University of Puerto Rico, Mayaguez (Puerto Rico). $3 1017811
773 0 $t Masters Abstracts International $g 43-01.
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790 $a 0553
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792 $a 2004
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