Automated Change Detection for Synthetic Aperture Sonar

Abstract

In this paper, an automated change detection technique is presented that compares new and historical seafloor images created with sidescan synthetic aperture sonar (SAS) for changes occurring over time. The method consists of a four stage process: a coarse navigational alignment; fine-scale co-registration using the scale invariant feature transform (SIFT) algorithm to match features between overlapping images; sub-pixel co-registration to improves phase coherence; and finally, change detection utilizing canonical correlation analysis (CCA). The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. By using precise co-registration tools and change detection algorithms, it is shown that the coherent nature of the SAS data can be exploited and utilized in this environment over time scales ranging from hours through several days.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
ADA601363

Entities

People

  • Bradley Marchand
  • Daniel D. Sternlicht
  • J. D. Tucker
  • Mahmood R. Azimi-sadjadi
  • Tesfaye G-michael
  • Timothy M. Marston

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Autonomous Underwater Vehicles
  • Change Detection
  • Correlation Analysis
  • Data Science
  • Data Sets
  • Databases
  • Detection
  • Environment
  • Frequency
  • Information Science
  • Navigation
  • Radar
  • Seabed
  • Statistical Analysis
  • Synthetic Aperture Radar
  • Synthetic Aperture Sonar

Readers

  • Coastal Oceanography
  • Computer Vision.