Efficacy of Frequency on Detecting Targets in Foliage Using Incoherent Change Detection

Abstract

The effectiveness of applying incoherent change detection to multipass synthetic aperture radar (SAR) images and targets in foliage is affected by the operating radar frequency band. Incoherent change detection is achieved by taking the weighted difference of the magnitude of two well registered passes of SAR imagery. Items which change between two passes, such as a target present in the first pass and not present in the second pass, will appear in the weighted difference image. With well registered wideband SAR imagery, images can be divided into frequency bands and evaluated using incoherent change detection. An Environmental Research Institute of Michigan (ERIM) Rail SAR experiment provides such a data collection. The Rail SAR is characterized by polarimetric, wideband (400 MHz -1.3 GHz), multipass (with and without targets), well registered SAR images. The ERIM Rail SAR data is divided into a number of frequency bands which simulate the high-band Stanford Research Institute, International (SRI) Ultra-WideBand Radar (UWBR) (350-550 MHz), the Loral miniature SAR (MSAR) (500-800 MHz), and the Naval Air Warfare Center (NAWC) P-3 upgraded UWBR (200-900 MHz) sensor. This paper shows how these sensors work on targets in foliage using incoherent change detection and provides an experimental measurement of upper-bound performance.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1994
Accession Number
ADA282090

Entities

People

  • Clark R. Hendrickson
  • Robert R. James

Organizations

  • Naval Command, Control and Ocean Surveillance Center

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Aerial Warfare
  • Aircrafts
  • Bandwidth
  • Change Detection
  • Data Fusion
  • Detection
  • Detectors
  • False Alarms
  • Frequency
  • Frequency Bands
  • Recognition
  • Standards
  • Surveillance
  • Synthetic Aperture Radar
  • Target Detection
  • Two Dimensional
  • Vehicles

Readers

  • Computer Vision.
  • Radar Systems Engineering.