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Component Analysis-Based Change Detection for Sea Floor Imagery and Prelude to Sea-Surface Object Detection

Title: Component Analysis-Based Change Detection for Sea Floor Imagery and Prelude to Sea-Surface Object Detection.
Name(s): G-Michael, Tesfaye, author
Roberts, Rodney G., professor directing dissertation
Meyer-Bäse, Anke, university representative
Meyer-Baese, U., 1964-, committee member
Foo, Simon Y., committee member
Florida State University, degree granting institution
College of Engineering, degree granting college
Department of Electrical and Computer Engineering, degree granting department
Type of Resource: text
Genre: Text
Doctoral Thesis
Issuance: monographic
Date Issued: 2017
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (147 pages)
Language(s): English
Abstract/Description: In undersea remote sensing change detection is the process of detecting changes from pairs of multi-temporal sonar images of the seafloor that are surveyed approximately from the same location. The problem of change detection, subsequent anomaly feature extraction, and false alarms reduction is complicated due to several factors such as the presence of random speckle pattern in the images, variability in the seafloor environmental conditions, and platform instabilities. These complications make the detection and classification of targets difficult. This thesis presents the first successful development of an end-to-end automated seabed change detection using multi-temporal synthetic aperture sonar (SAS) imagery that include a false detection/false alarms reduction based on principal and independent component analysis (PCA/ICA). ICA is a well-established statistical signal processing technique that aims to decompose a set of multivariate signals, i.e., SAS images, into a basis of statistically independent data-vectors with minimal loss of information content. The goal of ICA is to linearly transform the data such that the transformed variables are as statistically independent from each other as possible. The changes in the scene are detected in reduced second or higher order dependencies by ICA. Thus removing dependencies will leave the change features that will be further analyzed for detection and classification. Test results of the proposed method on a data set of SAS images (snippets) of declared changes from an automated change detection (ACD) process will be presented. These results illustrate the effectiveness of component analysis for reduction of false alarms in ACD process. In the context of sea surface object detection, this thesis investigates bistatic radar engagement using synthetic aperture radar (SAR) and examines five models of the bistatic electromagnetic scattering that will support future research on SAR sea-surface change detection.
Identifier: 2018_Sp_GMichael_fsu_0071E_14301 (IID)
Submitted Note: A Dissertation submitted to the Department of Electrical and Computer Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Fall Semester 2017.
Date of Defense: November 21, 2017.
Keywords: ACD, CCD, Change detection, Electromagnetic scattering, ICA, synthetic aperture sonar
Bibliography Note: Includes bibliographical references.
Advisory Committee: Rodney G. Roberts, Professor Directing Dissertation; Anke Meyer-Baese, University Representative; Uwe H. Meyer-Baese, Committee Member; Simon Y. Foo, Committee Member.
Subject(s): Electrical engineering
Persistent Link to This Record:
Host Institution: FSU

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G-Michael, T. (2017). Component Analysis-Based Change Detection for Sea Floor Imagery and Prelude to Sea-Surface Object Detection. Retrieved from