東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Remote Sensing of Open Water Fractio...

Ortiz, Macarena D.

Linked to FindBook

Google Book

Amazon

博客來

Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms./
Author:	Ortiz, Macarena D.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	191 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
Subject:	Climate change. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10622254
ISBN:	9780355242126

Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms.
Ortiz, Macarena D.

Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 191 p.

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)--University of Miami, 2017.

Classification of open water fraction (OWF) from synthetic aperture radar (SAR) images in the marginal ice zone can be significantly difficult during the summer months, where melt-onset can alter the backscatter and melt ponds contaminate OWF estimates. In this dissertation, we explore five different machine learning algorithms including Neural Networks, Linear Support Vector Machines, Naive Bayes, K-Nearest Neighbor and Discriminant Analysis to quantify OWF using TerraSAR-X Stripmap images during the boreal summer of 2014. To validate our methods, we use nearly-coincident high resolution panchromatic optical images. We find that overall, the classification algorithms attained comparable accuracies, however the Naive Bayes achieved the fastest computation time. Faster computation can be very practical for users on vessels wishing to have accurate "on-the-fly" methods to calculate ice/water from SAR for navigational purposes and for modelers working with near real-time ice forecasting. We also present a prototype algorithm using linear support vector machines designed to quantify the evolution of melt pond fraction from the optical dataset in an area where several in-situ instruments were deployed by the British Antarctic Survey and the Marginal Ice Zone Program, during April-September 2014. We explore both the temporal evolution of melt ponds and spatial statistics such as pond fraction, pond area, and number pond density, to name a few. We also introduce a linear regression model that can potentially be used to estimate average pond area by ingesting several melt pond statistics and shape parameters.

ISBN: 9780355242126Subjects--Topical Terms:

2079509
Climate change.

Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms.
LDR:02622nmm a2200313 4500 001 2158827
005 20180618102630.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355242126
035 $a (MiAaPQ)AAI10622254
035 $a (MiAaPQ)miami:11517
035 $a AAI10622254
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ortiz, Macarena D. $3 3346670
245 1 0 $a Remote Sensing of Open Water Fraction and Melt Ponds in the Beaufort Sea Using Machine Learning Algorithms.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 191 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
500 $a Adviser: Hans C. Graber.
502 $a Thesis (Ph.D.)--University of Miami, 2017.
520 $a Classification of open water fraction (OWF) from synthetic aperture radar (SAR) images in the marginal ice zone can be significantly difficult during the summer months, where melt-onset can alter the backscatter and melt ponds contaminate OWF estimates. In this dissertation, we explore five different machine learning algorithms including Neural Networks, Linear Support Vector Machines, Naive Bayes, K-Nearest Neighbor and Discriminant Analysis to quantify OWF using TerraSAR-X Stripmap images during the boreal summer of 2014. To validate our methods, we use nearly-coincident high resolution panchromatic optical images. We find that overall, the classification algorithms attained comparable accuracies, however the Naive Bayes achieved the fastest computation time. Faster computation can be very practical for users on vessels wishing to have accurate "on-the-fly" methods to calculate ice/water from SAR for navigational purposes and for modelers working with near real-time ice forecasting. We also present a prototype algorithm using linear support vector machines designed to quantify the evolution of melt pond fraction from the optical dataset in an area where several in-situ instruments were deployed by the British Antarctic Survey and the Marginal Ice Zone Program, during April-September 2014. We explore both the temporal evolution of melt ponds and spatial statistics such as pond fraction, pond area, and number pond density, to name a few. We also introduce a linear regression model that can potentially be used to estimate average pond area by ingesting several melt pond statistics and shape parameters.
590 $a School code: 0125.
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Physical oceanography. $3 3168433
650 4 $a Artificial intelligence. $3 516317
690 $a 0404
690 $a 0415
690 $a 0800
710 2 $a University of Miami. $b Applied Marine Physics. $3 3174198
773 0 $t Dissertation Abstracts International $g 79-02B(E).
790 $a 0125
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10622254