Process for the Development of Image Quality Metrics for Underwater Electro-Optic Sensors

Abstract

Electro-optic identification "EOID" sensors have been demonstrated as an important tool in the identification of bottom sea mines and are transitioning to the fleet. These sensors produce two and three-dimensional images that will be used by operators to make the all-important decision regarding use of neutralization systems against sonar contacts classified as mine-like. The quality of EOID images produced can vary dramatically depending on system design, operating parameters, and ocean environment, necessitating the need for a common scale of image quality or interpretability as a basic measure of the information content of the output images and the expected performance that they provide. Two candidate approaches have been identified for the development of an image quality metric. The first approach is the development of a modified National Imagery Interpretability Rating Scale "NIIRS" based on the EOID tasks. Coupled with this new scale would be a modified form of the General Image Quality Equation "GIQE" to provide a bridge from the system parameters to the NIIRS scale. The other approach is based on the Target Acquisition Model "TAM" based on Johnson's criteria and a set of tasks. The following paper presents these two approaches along with an explanation of the application to the EOID problem.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2002
Accession Number
ADA479043

Entities

People

  • Ann Domnich
  • Brett Cordes
  • James S. Taylor Jr.
  • Sam Osofsky

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Altitude
  • Detection
  • Detectors
  • Electronic Mail
  • Equations
  • Geometry
  • High Resolution
  • Identification
  • Identification Systems
  • Lasers
  • Naval Mines
  • Oceans
  • Synthetic Aperture Radar
  • Target Acquisition
  • Three Dimensional
  • Two Dimensional

Readers

  • Image Processing and Computer Vision.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design