Date of Award

12-1991

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Steven K. Rogers, PhD

Abstract

In this thesis, three approaches were used for Automatic Target Recognition (ATR). These approaches were shape, moment and Fourier generated features, Karhunen-Loeve transform (KLT) generated features and Discrete Cosine Transform (DCT) generated features. The KLT approach was modelled after the face recognition research by Suarez, AFIT, and Turk and Pentland, MIT. A KLT is taken of a reduced covariance matrix, composed all three classes of targets, and the resulting eigenimages are used to reconstruct the original images. The reconstruction coefficients for each original image are found by taking the dot product of the original image with each eigenimage. These reconstruction coefficients were implemented as features into a three layer backprop with momentum network. Using the hold-one-cut-out technique of testing data, the net could correctly differentiate the targets 100% of the time. Using the hold one- cut-out technique of testing data, the net could correctly differentiate the targets 100% of the time. Using standard features, the correct classification rate was 99.33%. The DCT was also taken of each image, and 16 low frequency Fourier components were kept as features. These recognition rates were compared to FFT results where each set contained the top five feature, as determined by a saliency test. The results proved that the DCT and the FFT were equivalent concerning classification of targets.

AFIT Designator

AFIT-GE-ENG-91D-49

DTIC Accession Number

ADA243888

Comments

The author's Vita page is omitted.

Download

Share

COinS