A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Vector quantization and signal compression
Vector quantization and signal compression
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition by Self-Organizing Neural Networks
Pattern Recognition by Self-Organizing Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
EURASIP Journal on Applied Signal Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
A genetic procedure used to train RFB neural networks
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Superresolution versus motion compensation-based techniques for radar imaging defense applications
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Subinteger range-bin alignment method for ISAR imaging of noncooperative targets
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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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.