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Three Dimensional Interpretation of Sonar Image for Fisheries Research

  • Kohji Iida
  • Tohru Mukai
  • Yoshinao Aoki
  • Tomoko Hayakawa
Part of the Acoustical Imaging book series (ACIM, volume 22)

Abstract

Three-dimensional interpretation of fish images from a sector scanning sonar to determine the shape and the distribution of fish schools was investigated. The sonar drove a half-circular cylindrical array transducer, which was mechanically rotatable on two axes. It emitted 2ms acoustic pulses of 160kHz in a half-circular plane omnidirectionally, then received echoes by scanning the insonified plane. In general use, the scanning plane is tilted a few degree downward from the surface, and the fish school echoes are indicated on a display as a PPI image around the ship. Therefore we can only observe fish echoes as a two-dimensional image, even if the scanning plane is tilted largely from the surface.

The new idea we discuss here is to reconstruct a three-dimensional image from hundreds of sectional sonar images using a computer image processing technique. In this method, the scanning plane is set vertically downward and perpendicular to the ship’s course. Two hundred successive sectional images were digitized and the three-dimensional information was displayed as a top view projection and a side view projection along the ship,s course, this method of data processing allows the shape and the distribution of fish schools and bottom features to be easily understood.

A fisheries survey using sonar was conducted near artificial fish reefs located at about one hundred meters depth. Many fish-school echoes were observed and the sonar images were analyzed. While the general fishery echo sounder provides information only for fish schools directly below a ship, this new method is a promising technique to obtain three-dimensional information from fisheries surveys.

Keywords

Swimming Behavior Fish School Sonar Image Scanning Plane View Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Kohji Iida
    • 1
  • Tohru Mukai
    • 1
  • Yoshinao Aoki
    • 2
  • Tomoko Hayakawa
    • 2
  1. 1.Department of Fisheries ScienceHokkaido UniversityHakodateJapan
  2. 2.Department of Information EngineeringHokkaido UniversitySapporoJapan

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