Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery

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Contact Information	Please send any questions regarding this item to libref@uno.edu
Title	Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery
Author	Gendron, Marlin
Document Type	Dissertation
Keywords	Change detection; image processing; sonar; acoustics; automated algorithms
Abstract	During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author’s Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3 – 48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author’s repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the CAS performs geospatial searching 1.75x faster on large data sets. Finally, the concluding chapter of this dissertation gives important details on how the completed ACDC system will function, and discusses the author’s future research to develop additional algorithms and data structures for ACDC.
Access	Unrestricted
Degree	Ph. D.
Degree Program	Engineering and Applied Science
Department	Dept. of Physics
Major Professor	Ioup, Juliette
Advisory Committee	Bourgeois, Brian; Bourgeois, Edit; Ioup, George; Seab,Greg; Kalcic, Maria
Date Degree Awarded	2004-12-17; *
Format	PDF
URL	See 'reference url' on top or bottom navigation bars.
Relation	http://louisdl.louislibraries.org/NOD/Pages/home.php
Rights	The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.
Catalog Date	2006-09-21; *
Object File Name	nd000152
Viewer Information	Adobe Reader required for viewing (http://www.adobe.com/products/acrobat/readermain.html)
File Size	3403.593 KB
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Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery

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