A Comparison of Feature Detectors for Underwater Sonar Imagery

Peter Tueller, Ryan Kastner, Roee Diamant

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


In this work we compare the performance of seven popular feature detection algorithms on a synthetic sonar image dataset. The dataset consists of a single mine-like object (MLO) superimposed on three different backgrounds: grass, sand ripple, and sand. We explore the performance of Harris, Shi-Tomasi, SIFT, SURF, STAR, FAST, and ORB on each of these backgrounds, and all the backgrounds at once by training an SVM classifier. Performance is evaluated with ROC curves by comparing the number of correctly identified features belonging to objects (True Positives) and the number of incorrectly identified features belonging to background noise (False Positives).

Original languageEnglish
Title of host publicationOCEANS 2018 MTS/IEEE Charleston, OCEAN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538648148
StatePublished - 7 Jan 2019
EventOCEANS 2018 MTS/IEEE Charleston, OCEANS 2018 - Charleston, United States
Duration: 22 Oct 201825 Oct 2018

Publication series

NameOCEANS 2018 MTS/IEEE Charleston, OCEAN 2018


ConferenceOCEANS 2018 MTS/IEEE Charleston, OCEANS 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2018 IEEE.


  • Feature detection
  • Sonar
  • Visual odometry

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Oceanography


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