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Proceedings Paper

Feature-based classification of SAR data using RBF networks
Author(s): Batuhan Ulug; Jun Zhao; Stanley C. Ahalt
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Paper Abstract

We describe the application of radial basis function (RBF) classifiers to feature-based automatic target recognition (FBATR) using synthetic aperture radar (SAR) data. FBATR systems are attractive because of their promise for robust, computationally efficient, scalable ATR systems. We compare the performance of RBF classifiers, multi layer perceptron (MLP) networks and a nearest neighbor (1-NN) classifier using a synthetic SAR database. Using this database, this preliminary study attempts to establish how classification performance deteriorates when the measured data is perturbed with additive white Gaussian noise (AWGN) prior to feature extraction. Our experimental results indicate that the RBF network performs better and it is more robust to this type of noise when compared to the other feature-based classifiers we considered. Consequently, we conclude that RBF classifiers are strong candidates for FBATR systems.

Paper Details

Date Published: 5 July 1995
PDF: 12 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213052
Show Author Affiliations
Batuhan Ulug, The Ohio State Univ. (United States)
Jun Zhao, The Ohio State Univ. (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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