Improved supervised classification of underwater military munitions using height features derived from optical imagery

Arthur C.R. Gleason, A. S.M. Shihavuddin, Nuno Gracias, Gregory Schultz, Brooke E. Gintert

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


A supervised classification routine was used to classify munitions targets and basic seabed types from underwater images. Additional features that were based on the local relief, or height, of the seabed were then added to the classifier and new results computed using the expanded feature set. The height data were generated from the input images themselves using structure-from-motion computer vision techniques. The initial image classifier was shown to distinguish munitions from non-munitions (background) with generally > 80% accuracy except that many false positive matches for munitions were observed. Extending the algorithm to also use height data derived from stereo reconstruction showed that incorporating such 2.5-D data greatly improved the classification results. Using the 2.5-D information reduced the number of false positives. Furthermore, improved accuracy was observed not only on the basic, binary munitions / non-munitions classes. Adding 2.5-D information also improved the capability to discriminate different types of munitions from one another.

Original languageEnglish (US)
Title of host publicationOCEANS 2015 - MTS/IEEE Washington
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780933957435
StatePublished - Feb 8 2016
EventMTS/IEEE Washington, OCEANS 2015 - Washington, United States
Duration: Oct 19 2015Oct 22 2015

Publication series

NameOCEANS 2015 - MTS/IEEE Washington


OtherMTS/IEEE Washington, OCEANS 2015
Country/TerritoryUnited States


  • 2.5-D features
  • seabed classification
  • underwater military munitions

ASJC Scopus subject areas

  • Signal Processing
  • Oceanography
  • Ocean Engineering
  • Instrumentation
  • Acoustics and Ultrasonics


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