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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Deep Neural Network for Real Time Mesoscale Ocean Eddy Detection./
Author:	Witherell, Brianna Kimberly.
Description:	1 online resource (86 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Remote sensing. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (86 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379527808Subjects--Topical Terms:

557272
Remote sensing.
Subjects--Index Terms:

Artificial intelligenceIndex Terms--Genre/Form:

554714
Electronic books.

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