Accession Number:

ADA230580

Title:

Gabor Filters and Neural Networks for Segmentation of Synthetic Aperture Radar Imagery

Descriptive Note:

Master's thesis

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING

Personal Author(s):

Report Date:

1990-12-01

Pagination or Media Count:

123.0

Abstract:

This research investigates Gabor filters and artificial networks for autonomous segmentation of 1 foot by 1 foot high resolution polarimetric synthetic aperture radar SAR. Processing involved frequency correlation between the SAR imagery and biologically motivated Gabor functions. Methods for selecting the Gabor tuning parameters from the endless choices of frequency, rotation, standard deviation and bandwidth are discussed. Using these parameters, resulting Gabor correlation images were reduced in speckle, and more detailed. This research used cosine Gabor functions and operated on single polarization HH magnitude data. Following selection of the appropriate Gabor features, multiple Gabor representations were generated and converted for ANN training. Networks investigated were the Kohonen and radial basis function RBF algorithms. Provided are results demonstrating a Kohonen network calibration technique and how combination of Gabor processing and RBF networks provide scene segmentation.

Subject Categories:

  • Cybernetics
  • Active and Passive Radar Detection and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE