Feature-based classification of SAR data using RBF networks

Batuhan Ulug; Jun Zhao; Stanley C. Ahalt

doi:10.1117/12.213052

5 July 1995 Feature-based classification of SAR data using RBF networks

Batuhan Ulug, Jun Zhao, Stanley C. Ahalt

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213052
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Batuhan Ulug, Jun Zhao, and Stanley C. Ahalt "Feature-based classification of SAR data using RBF networks", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213052

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