Radar target recognition using contourlet packet transform and neural network approach

被引：22

作者：

Yang, Shuyuan ^{[1
]}

Wang, Min ^{[2
]}

Jiao, Licheng ^{[1
]}

机构：

[1] Xidian Univ, Dept Elect Engn, Inst Intelligent Informat Proc, Xian 710071, Peoples R China

[2] Xidian Univ, Dept Elect Engn, Natl Key Lab Radar Signal Proc, Xian 710071, Peoples R China

来源：

SIGNAL PROCESSING | 2009年 / 89卷 / 04期

基金：

中国博士后科学基金; 美国国家科学基金会;

关键词：

Adaptive contourlet packet; Target recognition; Genetic algorithm; RBFNN; CLASSIFICATION; RIDGELET;

D O I：

10.1016/j.sigpro.2008.09.015

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. (C) 2008 Elsevier B.V. All rights reserved,

引用

页码：394 / 409

页数：16

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