Mine-Like Objects detection in Side-Scan Sonar images using a shadows-highlights geometrical features space

Azriel Sinai, Alon Amar, Guy Gilboa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We suggest a method to detect mine-like objects in side scan sonar images. First, we use the well known K-means algorithm recognizes which recognizes the presence of mine-like segments, followed by the Chan-Vese active contour algorithm to sharp and restore the edges of the suspected segments. Then, we convert the image into a shadows and highlights map based on geometrical features of the connectivity between shadows and highlights. By exploiting the unique geometrical features of mine-like objects, we implement a Neyman-Pearson test to detect the mine-like objects given the connectivity map while reducing false alarms and other artifacts. Testing on real data images show that the suggested approach have good detection results.

Original languageEnglish
Title of host publicationOCEANS 2016 MTS/IEEE Monterey, OCE 2016
ISBN (Electronic)9781509015375
DOIs
StatePublished - 28 Nov 2016
Event2016 OCEANS MTS/IEEE Monterey, OCE 2016 - Monterey, United States
Duration: 19 Sep 201623 Sep 2016

Publication series

NameOCEANS 2016 MTS/IEEE Monterey, OCE 2016

Conference

Conference2016 OCEANS MTS/IEEE Monterey, OCE 2016
Country/TerritoryUnited States
CityMonterey
Period19/09/1623/09/16

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Oceanography
  • Ocean Engineering

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