Adaptive Automated Detection for Synthetic Aperture Sonar Images Using Seabed Classification

Abstract

In this work we have shown an initial method for using sensed environmental features to predict automatic target recognition (ATR) performance. Although we have applied this method to a cascade detector, the work can be extended to other detector/classifier methods such as a matched filter. As with all trained methods, generalization is the critical measure of performance. The utility of the trained method is in its ability to effectively detect targets in a previously unseen area. Using the data sets, we have shown the effective generalization performance of the trained cascade detector.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
AD1003590

Entities

People

  • J. A. Fawcett
  • W. A. Connors

Organizations

  • Defence Research and Development Canada

Tags

DTIC Thesaurus Topics

  • Acoustics
  • Classification
  • Data Sets
  • Detection
  • Detectors
  • False Alarms
  • High Resolution
  • Matched Filters
  • Recognition
  • Remote Sensing
  • Sonar Images
  • Synthetic Aperture Sonar
  • Target Recognition
  • Underwater Acoustics
  • Warning Systems

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Computer Vision.
  • Radar Systems Engineering.