Underwater mine detection using symbolic pattern analysis of sidescan sonar images

Chinmay Rao, Kushal Mukherjee, Shalabh Gupta, Asok Ray, Shashi Phoha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

This paper presents symbolic pattern analysis of sidescan sonar images for detection of mines and mine-like objects in the underwater environment. For robust feature extraction, sonar images are symbolized by partitioning the data sets based on the information generated from the ground truth. A binary classifier is constructed for identification of detected objects into mine-like and non-mine-like categories. The pattern analysis algorithm has been tested on sonar data sets in the form of images, which were provided by the Naval Surface Warfare Center. The algorithm is designed for real-time execution on limited-memory commercial-of-the-shelf platforms, and is capable of detecting seabed-bottom objects and vehicle-induced image artifacts.

Original languageEnglish (US)
Title of host publication2009 American Control Conference, ACC 2009
Pages5416-5421
Number of pages6
DOIs
StatePublished - Nov 24 2009
Event2009 American Control Conference, ACC 2009 - St. Louis, MO, United States
Duration: Jun 10 2009Jun 12 2009

Other

Other2009 American Control Conference, ACC 2009
CountryUnited States
CitySt. Louis, MO
Period6/10/096/12/09

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Rao, C., Mukherjee, K., Gupta, S., Ray, A., & Phoha, S. (2009). Underwater mine detection using symbolic pattern analysis of sidescan sonar images. In 2009 American Control Conference, ACC 2009 (pp. 5416-5421). [5160102] https://doi.org/10.1109/ACC.2009.5160102