Approximation with brushlet systems
Journal of Approximation Theory
Rotation invariant feature extraction using Ridgelet and Fourier transforms
Pattern Analysis & Applications
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Shiftable multiscale transforms
IEEE Transactions on Information Theory - Part 2
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Guest Editorial Introduction To The Special Issue On Automatic Target Detection And Recognition
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Best wavelet packet bases in a rate-distortion sense
IEEE Transactions on Image Processing
Face recognition by curvelet based feature extraction
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Evolutionary RBF classifier for polarimetric SAR images
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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Contourlet is a ''true'' two-dimensional transform that captures the intrinsic geometrical structure and have been shown to be successful for many tasks in image processing. In this paper, a wavelet-based contourlet packet (WBCP) transform is investigated and an adaptive contourlet packet (ACP) transform based on genetic algorithm (GA) is proposed to extract the features of radar targets in synthetic aperture radar (SAR) images recognition. The features of the sampled targets are subsequently used to train a radical basis function neural network (RBFNN) that is then able to quickly and reliably recognize the objects. In comparison with WBCP, our proposed ACP has relatively low computational complexity and high recognition rate. Finally, we show some numerical experiments demonstrating the potential of this method for target recognition in SAR image processing.