Application of fusion algorithms for computer aided detection and classification of bottom mines to synthetic aperture sonar test data

@inproceedings{Ciany2006ApplicationOF,
  title={Application of fusion algorithms for computer aided detection and classification of bottom mines to synthetic aperture sonar test data},
  author={Charles M. Ciany and William C. Zurawski},
  booktitle={SPIE Defense + Commercial Sensing},
  year={2006}
}
Over the past several years, Raytheon Company has adapted its Computer Aided Detection/Computer-Aided Classification (CAD/CAC) algorithm to process side-scan sonar imagery taken in both the Very Shallow Water (VSW) and Shallow Water (SW) operating environments. This paper describes the further adaptation of this CAD/CAC algorithm to process Synthetic Aperture Sonar (SAS) image data taken by an Autonomous Underwater Vehicle (AUV). The tuning of the CAD/CAC algorithm for the vehicle's sonar is… CONTINUE READING

Results and Topics from this paper.

Key Quantitative Results

  • Similar to a previous study13, the remaining FFM algorithms achieved nearly the same performance, yielding a 21.2:1 FAR reduction at the 86.5% probability of correct classification for the AUV SAS data set.

References

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