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

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1990
Accession Number
ADA230580

Entities

People

  • Albert P. L'homme

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Computer Programming
  • Computer Science
  • Computer Vision
  • Detectors
  • Frequency Bands
  • High Resolution
  • Image Processing
  • Image Recognition
  • Information Processing
  • Neural Networks
  • Pattern Recognition
  • Processing Equipment
  • Recognition
  • Target Recognition
  • Training
  • Two Dimensional

Readers

  • Computer Vision.
  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks