Robust Sonar ATR with pose corrected sparse reconstruction-based classification

John McKay, Vishal Monga, Raghu Raj

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

2 Scopus citations


Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS). Because of this, sophisticated classification techniques originally developed for other tasks can be used in Sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC) which has shown an impressive resiliency to noise, blur, and occlusion even in settings with little training. We present a coherent strategy for using SRC for Sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric arrangements. Our method, pose corrected sparsity (PCS), incorporates state-of-the-art dictionary learning schemes on localized block extractions which we show produces compelling classification results on the RAWSAS dataset.

Original languageEnglish (US)
Title of host publicationOCEANS 2016 MTS/IEEE Monterey, OCE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509015375
StatePublished - Nov 28 2016
Event2016 OCEANS MTS/IEEE Monterey, OCE 2016 - Monterey, United States
Duration: Sep 19 2016Sep 23 2016

Publication series

NameOCEANS 2016 MTS/IEEE Monterey, OCE 2016


Other2016 OCEANS MTS/IEEE Monterey, OCE 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Oceanography
  • Ocean Engineering


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