Date of Award
2010
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Abstract
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.
Extent
xxi, 150 p. : ill. (some col.)
Recommended Citation
Brisson, Lisa Nicole, "methodology to detect and classify underwater unexploded ordnance in DIDSON sonar images" (2010). Electronic Theses and Dissertations 2010 - 2019. 131.
https://digitalcommons.fau.edu/etd_general_6/131
Identifier
FADT2683533
Comments
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.|by Lisa Nicole Brisson.