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Objective front detection from ocean...

Crock, Nathan.

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Objective front detection from ocean color data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Objective front detection from ocean color data./
Author:	Crock, Nathan.
Description:	57 p.
Notes:	Source: Masters Abstracts International, Volume: 52-05.
Contained By:	Masters Abstracts International52-05(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1552810
ISBN:	9781303739552

Objective front detection from ocean color data.
Crock, Nathan.

Objective front detection from ocean color data. - 57 p.

Source: Masters Abstracts International, Volume: 52-05.

Thesis (M.S.)--The Florida State University, 2013.

This item must not be sold to any third party vendors.

We outline a new approach to objectively locate and define mesoscale oceanic features from satellite derived ocean color data. Modern edge detection algorithms are robust and accurate for most applications, oceanic satellite observations however introduce challenges that foil many differentiation based algorithms. The clouds, discontinuities, noise, and low variability of pertinent data prove confounding. In this work the input data is first quantized using a centroidal voronoi tesselation (CVT), removing noise and revealing the low variable fronts of interest. Clouds are then removed by assuming values of its surrounding neighbors, and the perimeters of these resulting cloudless regions localize the fronts to a small set. We then use the gradient of the quantized data as a compass to walk around the front and periodically select points to be knots for a Hermite spline. These Hermite splines yield an analytic representation of the fronts and provide practitioners with a convenient tool to calibrate their models.

ISBN: 9781303739552Subjects--Topical Terms:

523869
Computer science.

Objective front detection from ocean color data.
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020 $a 9781303739552
035 $a (MiAaPQ)AAI1552810
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100 1 $a Crock, Nathan. $3 3175711
245 1 0 $a Objective front detection from ocean color data.
300 $a 57 p.
500 $a Source: Masters Abstracts International, Volume: 52-05.
500 $a Advisers: Gordon Erlebacher; Eric Chassignet.
502 $a Thesis (M.S.)--The Florida State University, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a We outline a new approach to objectively locate and define mesoscale oceanic features from satellite derived ocean color data. Modern edge detection algorithms are robust and accurate for most applications, oceanic satellite observations however introduce challenges that foil many differentiation based algorithms. The clouds, discontinuities, noise, and low variability of pertinent data prove confounding. In this work the input data is first quantized using a centroidal voronoi tesselation (CVT), removing noise and revealing the low variable fronts of interest. Clouds are then removed by assuming values of its surrounding neighbors, and the perimeters of these resulting cloudless regions localize the fronts to a small set. We then use the gradient of the quantized data as a compass to walk around the front and periodically select points to be knots for a Hermite spline. These Hermite splines yield an analytic representation of the fronts and provide practitioners with a convenient tool to calibrate their models.
590 $a School code: 0071.
650 4 $a Computer science. $3 523869
650 4 $a Ocean engineering. $3 660731
690 $a 0984
690 $a 0547
710 2 $a The Florida State University. $b Computational Science. $3 2094133
773 0 $t Masters Abstracts International $g 52-05(E).
790 $a 0071
791 $a M.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1552810