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methodology to detect and classify underwater unexploded ordnance in DIDSON sonar images

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Date Issued:
2010
Summary:
High-resolution sonar systems are primarily used for ocean floor surveys and port security operations but produce images of limited resolution. In turn, a sonar-specific methodology is required to detect and classify underwater unexploded ordnance (UXO) using the low-resolution sonar data. After researching and reviewing numerous approaches the Multiple Aspect-Fixed Range Template Matching (MAFR-TM) algorithm was developed. The MAFR-TM algorithm is specifically designed to detect and classify a target of high characteristic impedance in an environment that contains similar shaped objects of low characteristic impedance. MAFR-TM is tested against a tank and field data set collected by the Sound Metrics Corp. DIDSON US300. This thesis document proves the MAFR-TM can detect, classify, orient, and locate a target in the sector-scan sonar images. This paper focuses on the MAFR-TM algorithm and its results.
Title: A methodology to detect and classify underwater unexploded ordnance in DIDSON sonar images.
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Name(s): Brisson, Lisa Nicole.
College of Engineering and Computer Science
Department of Ocean and Mechanical Engineering
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Issued: 2010
Publisher: Florida Atlantic University
Physical Form: electronic
Extent: xxi, 150 p. : ill. (some col.)
Language(s): English
Summary: High-resolution sonar systems are primarily used for ocean floor surveys and port security operations but produce images of limited resolution. In turn, a sonar-specific methodology is required to detect and classify underwater unexploded ordnance (UXO) using the low-resolution sonar data. After researching and reviewing numerous approaches the Multiple Aspect-Fixed Range Template Matching (MAFR-TM) algorithm was developed. The MAFR-TM algorithm is specifically designed to detect and classify a target of high characteristic impedance in an environment that contains similar shaped objects of low characteristic impedance. MAFR-TM is tested against a tank and field data set collected by the Sound Metrics Corp. DIDSON US300. This thesis document proves the MAFR-TM can detect, classify, orient, and locate a target in the sector-scan sonar images. This paper focuses on the MAFR-TM algorithm and its results.
Identifier: 651934106 (oclc), 2683533 (digitool), FADT2683533 (IID), fau:3507 (fedora)
Note(s): by Lisa Nicole Brisson.
Thesis (M.S.C.S.)--Florida Atlantic University, 2010.
Includes bibliography.
Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.
Subject(s): Ocean tomography
Unexploded ordnance -- Detection -- Methodology
Underwater acoustics
Signal processing -- Digital techniques
Persistent Link to This Record: http://purl.flvc.org/FAU/2683533
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU