A Contourlet-Based Method for Wavelet Neural Network Automatic Target Recognition

Authors:
Xue Mei;Liangzheng Xia;Jiuxian Li
Affiliations:
School of Automation Control, Southeast University, Nanjing, 210096, China and School of Automation Control, Nanjing University of Technology, Nanjing, 210096, China;School of Automation Control, Southeast University, Nanjing, 210096, China;School of Automation Control, Southeast University, Nanjing, 210096, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

An object recognition algorithm is put forward based on statistical character of contourlet transform and multi-object wavelet neural network (MWNN). A contourlet-based feature extraction method is proposed, which forms the feature vector taking advantage of the statistical attribution in each sub-band of contourlet transform. And then the extracted features are weighted according to their dispersion degree of data. WNN is used as classifier, which combines the extraction local singularity of wavelet transform and adaptive of artificial neural network. With the application in an aircraft recognition system, the experimental data showed the efficiency of this algorithm for automation target recognition.Keywords:Automatic target recpgnition, Wavelet neural network, Contourlet transform, Feature extraction.