Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment
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Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment Emanuel Radoi, Andre´ Quinquis, and Felix Totir ENSIETA, E3I2 Research Center, 2 rue Franc¸ois Verny, 29806 Brest, France Received 1 June 2005; Revised 30 January 2006; Accepted 5 February 2006 The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification) method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART), which is compared to two standard classifiers, MLP (multilayer perceptron) and fuzzy KNN (K nearest neighbors). While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure. Copyright © 2006 Emanuel Radoi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Our research work has been focused for several years on ISAR techniques and automatic target recognition (ATR) using superresolution radar imagery. The anechoic chamber of ENSIETA and the associated measurement facilities allow us to obtain radar signatures for various scale-reduced targets and to reconstruct their radar images using a turntable configuration. The main advantage of this type of configuration is the capability to achieve realistic measurements, to have a perfect control of the target configuration, and to simplify the interpretation of the obtained results. We have already presented in [1] some of our significant results on both theoretical and practical aspects related to the application of superresolution imagery techniques. Since a critical point for the application of these methods is the estimation of the number of scattering centers (the same as the signal subspace dimension), we have also proposed in [2] a discriminative learning-based algorithm to perform this task. The objective of this paper is to investigate another aspect, which is considered with increasing interest in the ATR field, that is, the automatic classification of ISAR images. This is a very challenging task for radar systems, which are generally designed to perform target detection and localization. The power of the backscattered signal, the receiver sensitivity, and the signal-to-noise ra
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