An Underwater Target Detection System for Electro-Optical Imagery Data

Abstract

The problem of detecting underwater targets from Electro-optical (EO) images is considered in this paper. A blockbased log-likelihood ratio test has been developed for detection and segmentation of underwater mine-like objects in the EO images captured with a CCD-based image sensor. The main focus of this research is to develop a robust detection algorithm that can be used to detect low contrast and partial underwater objects from the EO imagery with low false alarm rate. The detection method involves identifying frames of interest (FOI) containing the potential targets. Once the FOI have been identified, regions of interest (ROI) within the FOI are segmented from the background. Performance of the detection method is tested in terms of probability of detection, false alarm rate, and receiver operating characteristic (ROC) curves for FOI in the selected data runs. The algorithm shows promising results in target detection and generation of good silhouettes for subsequent classification.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2010
Accession Number
ADA527537

Entities

People

  • J. D. Tucker
  • Mahmood R. Azimi-sadjadi
  • Michael Kabatek

Organizations

  • Colorado State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computer Vision
  • Covariance
  • Data Sets
  • Databases
  • Detection
  • Detectors
  • Extraction
  • False Alarms
  • Feature Extraction
  • Machine Learning
  • Probability
  • Seabed
  • Target Detection
  • Underwater Targets
  • Warning Systems

Readers

  • Acoustical Oceanography.
  • Computer Vision.
  • Materials Science.

Technology Areas

  • AI & ML