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Shallow Water Object Detection, Characterization, and Localization Through Reflectivity Backscatter from Phasemeasuring Sidescan Sonar.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Shallow Water Object Detection, Characterization, and Localization Through Reflectivity Backscatter from Phasemeasuring Sidescan Sonar./
作者:	McCormack, Bryan P.
面頁冊數:	1 online resource (123 pages)
附註:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
標題:	Marine geology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29320444click for full text (PQDT)
ISBN:	9798351407869

Shallow Water Object Detection, Characterization, and Localization Through Reflectivity Backscatter from Phasemeasuring Sidescan Sonar.
McCormack, Bryan P.

Shallow Water Object Detection, Characterization, and Localization Through Reflectivity Backscatter from Phasemeasuring Sidescan Sonar. - 1 online resource (123 pages)

Source: Masters Abstracts International, Volume: 84-03.

Thesis (M.S.)--University of Massachusetts Boston, 2022.

Includes bibliographical references

Coastal waters are crucial to the socio-economic activities of humans, as well as to the health of our global oceans. Unfortunately, they have also long been a dumping ground with an 'out of sight, out of mind' mentality. Remote object differentiation in shallow water coastal environments is becoming increasingly important in the maintenance and restoration of these critical habitats. Detection, characterization, and localization (DCL) techniques are being developed around the use of the EdgeTech 6205 Phase-Measuring Sidescan Sonar (PMSS) in the coastal waters around Cape Cod, MA, USA. This instrument offers a unique look into the seafloor because of its ability to simultaneously collect co-located sidescan backscatter and bathymetry in extreme shallow water environments (<1m water depth). Inherent with the bathymetry is an uncalibrated amplitude backscatter dataset, referred to in this study as reflectivity backscatter. This reflectivity backscatter has been minimally used in current literature due to the coincidence of the sidescan backscatter dataset. This work aims to use the reflectivity backscatter to detect and differentiate between various objects on the seafloor, including unexploded ordnance (UXO), and placed marine debris such as derelict lobster pots, boat propellers, and car tires. The differentiation from multiple seabed types including sand, mud, and cobble, and different types of objects occurred through various statistical analysis methods including binomial and multinomial logistic regression (BLR, MLR). These methods have been applied to create statistical regression models for several variables including reflectivity backscatter amplitude, sounding distance from nadir, per-ping vessel roll, the orientation offset between per-ping vessel heading and object orientation, and all combinations of these variables. These statistical tests produced maximum likelihood odds ratios of individual soundings being associated with the various seabed and object types. Results from these analyses shows that DCL could be possible with reflectivity backscatter from PMSS, but significantly more testing is needed.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798351407869Subjects--Topical Terms:

3173821
Marine geology.
Subjects--Index Terms:

CharacterizationIndex Terms--Genre/Form:

542853
Electronic books.

Shallow Water Object Detection, Characterization, and Localization Through Reflectivity Backscatter from Phasemeasuring Sidescan Sonar.
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500 $a Source: Masters Abstracts International, Volume: 84-03.
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520 $a Coastal waters are crucial to the socio-economic activities of humans, as well as to the health of our global oceans. Unfortunately, they have also long been a dumping ground with an 'out of sight, out of mind' mentality. Remote object differentiation in shallow water coastal environments is becoming increasingly important in the maintenance and restoration of these critical habitats. Detection, characterization, and localization (DCL) techniques are being developed around the use of the EdgeTech 6205 Phase-Measuring Sidescan Sonar (PMSS) in the coastal waters around Cape Cod, MA, USA. This instrument offers a unique look into the seafloor because of its ability to simultaneously collect co-located sidescan backscatter and bathymetry in extreme shallow water environments (<1m water depth). Inherent with the bathymetry is an uncalibrated amplitude backscatter dataset, referred to in this study as reflectivity backscatter. This reflectivity backscatter has been minimally used in current literature due to the coincidence of the sidescan backscatter dataset. This work aims to use the reflectivity backscatter to detect and differentiate between various objects on the seafloor, including unexploded ordnance (UXO), and placed marine debris such as derelict lobster pots, boat propellers, and car tires. The differentiation from multiple seabed types including sand, mud, and cobble, and different types of objects occurred through various statistical analysis methods including binomial and multinomial logistic regression (BLR, MLR). These methods have been applied to create statistical regression models for several variables including reflectivity backscatter amplitude, sounding distance from nadir, per-ping vessel roll, the orientation offset between per-ping vessel heading and object orientation, and all combinations of these variables. These statistical tests produced maximum likelihood odds ratios of individual soundings being associated with the various seabed and object types. Results from these analyses shows that DCL could be possible with reflectivity backscatter from PMSS, but significantly more testing is needed.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Marine geology. $3 3173821
650 4 $a Acoustics. $3 879105
650 4 $a Remote sensing. $3 535394
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653 $a Detection
653 $a Localization
653 $a Phase-measuring sidescan sonar
653 $a Unexploded ordnance
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710 2 $a University of Massachusetts Boston. $b Marine Sciences and Technology (MS). $3 3542634
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