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A Deep Neural Network for Real Time ...

Witherell, Brianna Kimberly.

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A Deep Neural Network for Real Time Mesoscale Ocean Eddy Detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Deep Neural Network for Real Time Mesoscale Ocean Eddy Detection./
Author:	Witherell, Brianna Kimberly.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	86 p.
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Remote sensing. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30422198
ISBN:	9798379527808

A Deep Neural Network for Real Time Mesoscale Ocean Eddy Detection.
Witherell, Brianna Kimberly.

A Deep Neural Network for Real Time Mesoscale Ocean Eddy Detection. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 86 p.

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--Southern Illinois University at Edwardsville, 2023.

Accurate and timely detection of mesoscale ocean eddies is paramount to ensure the safety and efficiency of maritime vessels. Existing methods of mesoscale ocean eddy detection exist but are limited in three ways: infrequent passes over a region of interest, insufficient scan swath size, and inadequate data resolution. To combat these issues, this work presents and validates an artificial intelligence based mesoscale ocean eddy detection algorithm that is trained on a novel data set of several thousand eddies gathered from imagery from the GOES-16 Advanced Baseline Imager (ABI). The ABI onboard GOES-16 shows promise for resolving issues from polar-orbiting satellites for quick and accurate mesoscale ocean eddy detection.There are three major steps in this work: data collection and preprocessing, training the object detection scheme, and validating the object detection scheme. The pre-processed data is input into an object detection scheme, which is well trained with 43,776 files from 152 days from March-July of 2022. This work proposes a Faster R-CNN object detection scheme with a MobileNet V3 feature extraction network. Several competing object detection scheme's performances were compared including: Single Shot Multibox Detector, Fully Convolutional One-stage Object Detection, and Focal Loss. It was found the proposed Faster R-CNN approach with a MobileNet V3 model backbone shows optimal performance in both accuracy and robustness, allowing for real-time mesoscale ocean eddy detection.

ISBN: 9798379527808Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Artificial intelligence

A Deep Neural Network for Real Time Mesoscale Ocean Eddy Detection.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 86 p.
500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Wang, Yadong.
502 $a Thesis (M.S.)--Southern Illinois University at Edwardsville, 2023.
520 $a Accurate and timely detection of mesoscale ocean eddies is paramount to ensure the safety and efficiency of maritime vessels. Existing methods of mesoscale ocean eddy detection exist but are limited in three ways: infrequent passes over a region of interest, insufficient scan swath size, and inadequate data resolution. To combat these issues, this work presents and validates an artificial intelligence based mesoscale ocean eddy detection algorithm that is trained on a novel data set of several thousand eddies gathered from imagery from the GOES-16 Advanced Baseline Imager (ABI). The ABI onboard GOES-16 shows promise for resolving issues from polar-orbiting satellites for quick and accurate mesoscale ocean eddy detection.There are three major steps in this work: data collection and preprocessing, training the object detection scheme, and validating the object detection scheme. The pre-processed data is input into an object detection scheme, which is well trained with 43,776 files from 152 days from March-July of 2022. This work proposes a Faster R-CNN object detection scheme with a MobileNet V3 feature extraction network. Several competing object detection scheme's performances were compared including: Single Shot Multibox Detector, Fully Convolutional One-stage Object Detection, and Focal Loss. It was found the proposed Faster R-CNN approach with a MobileNet V3 model backbone shows optimal performance in both accuracy and robustness, allowing for real-time mesoscale ocean eddy detection.
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653 $a GOES
653 $a Mesoscale ocean eddy
653 $a Deep Neural Network
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