Date of Award
12-1990
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Department of Electrical and Computer Engineering
First Advisor
Steven K. Rogers, PhD
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.
AFIT Designator
AFIT-GE-ENG-90D-31
DTIC Accession Number
ADA230580
Recommended Citation
L'Homme, Albert P., "Gabor Filters and Neural Networks for Segmentation of Synthetic Aperture Radar Imagery" (1990). Theses and Dissertations. 7962.
https://scholar.afit.edu/etd/7962
Comments
The author's Vita page is omitted