東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Experiments in Optical Data Collecti...

Runyan, Hugh.

FindBook

Google Book

Amazon

博客來

Experiments in Optical Data Collection, Processing, and Analysis for Ocean Science.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Experiments in Optical Data Collection, Processing, and Analysis for Ocean Science./
作者:	Runyan, Hugh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	163 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
標題:	Ocean engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29994043
ISBN:	9798379418274

Experiments in Optical Data Collection, Processing, and Analysis for Ocean Science.
Runyan, Hugh.

Experiments in Optical Data Collection, Processing, and Analysis for Ocean Science. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 163 p.

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2023.

Improving oceanographic data collection involves two components: improving instrumentation, and improving the processing and analysis of resulting measurements. While advancing technology has improved and expanded data collection, processing these data has become a significant problem for under-resourced academic labs, rendering much of these data underutilized. The experiments described in this dissertation are contributions to the effort of improving data processing in oceanography. Chapter 1 examines parametrization of particle size distribution measurements in the Arctic, collected during decades of field expeditions by Dariusz Stramski, Rick Reynolds, and others. Experiments suggest that the commonly-used Junge-type power law model for parametrizing particle size distributions is insufficient, and cumulative distribution functions may offer a superior alternative. Particle size directly physically affects light, so the particle size distribution affects the signals of optical instruments. These results will increase the utility of satellite imagery, both by assisting the measurement of particle size from satellites, and by improving understanding of the impact of different seawater characteristics on optical signals. Chapter 2 discusses cutting-edge, technology-enabled survey image and 3D model techniques for studying coral reefs. The report focuses on lessons learned by the Sandin/Kuester labs from a decade of experience. Improving and standardizing data collection in the field allows research groups to pool datasets and compare results. Pooled databases are valuable for developing processing and analytical tools like neural networks, make those tools useful to more research groups, and enable ecological analysis at larger scales. Chapters 3 and 4 evaluate neural-network-assisted tools that can expedite taxonomic labeling of survey image products such as orthoprojections and pointclouds. Chapter 3 contains the first published investigation of 3D coral pointcloud segmentation with 3D neural networks. This new technology shows great promise, as evidenced by the time savings (36%) and high prediction accuracy in some contexts (~70-90%) recorded by our experiments, but requires more comprehensive, standardized datasets than what is currently available to fully develop or confidently evaluate. These tools are already capable of increasing the utility of survey imagery by expediting their annotation, which is necessary for many types of analysis, and further development will further improve their performance.

ISBN: 9798379418274Subjects--Topical Terms:

660731
Ocean engineering.
Subjects--Index Terms:

Computer vision

Experiments in Optical Data Collection, Processing, and Analysis for Ocean Science.
LDR:03718nmm a2200385 4500 001 2401982
005 20241028114723.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379418274
035 $a (MiAaPQ)AAI29994043
035 $a AAI29994043
035 $a 2401982
040 $a MiAaPQ $c MiAaPQ
100 1 $a Runyan, Hugh. $3 3772199
245 1 0 $a Experiments in Optical Data Collection, Processing, and Analysis for Ocean Science.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 163 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
500 $a Advisor: Sandin, Stuart;Kuester, Falko.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2023.
520 $a Improving oceanographic data collection involves two components: improving instrumentation, and improving the processing and analysis of resulting measurements. While advancing technology has improved and expanded data collection, processing these data has become a significant problem for under-resourced academic labs, rendering much of these data underutilized. The experiments described in this dissertation are contributions to the effort of improving data processing in oceanography. Chapter 1 examines parametrization of particle size distribution measurements in the Arctic, collected during decades of field expeditions by Dariusz Stramski, Rick Reynolds, and others. Experiments suggest that the commonly-used Junge-type power law model for parametrizing particle size distributions is insufficient, and cumulative distribution functions may offer a superior alternative. Particle size directly physically affects light, so the particle size distribution affects the signals of optical instruments. These results will increase the utility of satellite imagery, both by assisting the measurement of particle size from satellites, and by improving understanding of the impact of different seawater characteristics on optical signals. Chapter 2 discusses cutting-edge, technology-enabled survey image and 3D model techniques for studying coral reefs. The report focuses on lessons learned by the Sandin/Kuester labs from a decade of experience. Improving and standardizing data collection in the field allows research groups to pool datasets and compare results. Pooled databases are valuable for developing processing and analytical tools like neural networks, make those tools useful to more research groups, and enable ecological analysis at larger scales. Chapters 3 and 4 evaluate neural-network-assisted tools that can expedite taxonomic labeling of survey image products such as orthoprojections and pointclouds. Chapter 3 contains the first published investigation of 3D coral pointcloud segmentation with 3D neural networks. This new technology shows great promise, as evidenced by the time savings (36%) and high prediction accuracy in some contexts (~70-90%) recorded by our experiments, but requires more comprehensive, standardized datasets than what is currently available to fully develop or confidently evaluate. These tools are already capable of increasing the utility of survey imagery by expediting their annotation, which is necessary for many types of analysis, and further development will further improve their performance.
590 $a School code: 0033.
650 4 $a Ocean engineering. $3 660731
653 $a Computer vision
653 $a Coral
653 $a Neural network
653 $a Oceanography
653 $a Pointcloud
690 $a 0547
690 $a 0800
710 2 $a University of California, San Diego. $b Scripps Institution of Oceanography. $3 3543449
773 0 $t Dissertations Abstracts International $g 84-10B.
790 $a 0033
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29994043