一种多特征联合的地面SAR目标分层检测方法

doi:10.3969/j.issn.1001-2400.2016.02.016

西安电子科技大学学报 ›› 2016, Vol. 43 ›› Issue (2): 89-94.doi: 10.3969/j.issn.1001-2400.2016.02.016

一种多特征联合的地面SAR目标分层检测方法

曾丽娜1;周德云1;邢孟道2;张堃1

(1. 西北工业大学电子信息学院，陕西西安 710072;
2. 西安电子科技大学雷达信号处理国家重点实验室，陕西西安 710071)

收稿日期:2014-11-19 出版日期:2016-04-20 发布日期:2016-05-27
通讯作者: 曾丽娜
作者简介:曾丽娜(1982-)，女，西北工业大学博士研究生，E-mail: zenglina@mail.nwpu.edu.cn．
基金资助:
国家自然科学基金资助项目(61401363)

Novel SAR target detection algorithm via multiple features

ZENG Lina1;ZHOU Deyun1;XING Mengdao2;ZHANG Kun1

(1. School of Electronics and Information, Northwestern Polytechnical Univ., Xi'an 710072, China;
2. National Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an 710071, China)

Received:2014-11-19 Online:2016-04-20 Published:2016-05-27
Contact: ZENG Lina

摘要/Abstract

摘要：

提出了一种多特征联合的地面合成孔径雷达图像中装甲车等兴趣目标分层检测方法，根据反映目标真实物理性质的散射强度、尺寸以及与杂波差异等有效特征实现分层检测．研究提取尺寸特征、边界变化特征为兴趣目标有效特征，通过初步目标检测层和潜在目标鉴别层逐步剔除图像背景、自然杂波、人造杂波等非兴趣目标．有效特征提取能够在较少的特征数目条件下满足兴趣目标检测和鉴别的精度要求；分层处理能够在特征复杂度增加的情况下降低虚警检测和鉴别概率．与传统双参数恒虚警率、主成分分析等方法进行对比测试，从检测精度、检测效率方面验证了该方法的有效性．

关键词: 合成孔径雷达, 兴趣目标, 有效特征, 分层检测

Abstract:

A detection method for SAR targets based on combining multiple features is proposed. The targets of interest are detected according to the physical properties, which reflect the true characteristics including scattering intensity, size and differences from the clutter. By analyzing these characteristics, the size and boundary changes are determined as effective features. The image background, natural clutter, man-made clutter are eliminated in sequence using the developed detection algorithm, which contains two layers, namely, the initial target detection layer and the potential target identification layer. Effective features ensure that a smaller number of features are used to meet the precision of the target detection, and the discrimination detection method ensure that the probability of false alarm is reduced gradually with the increased complexity of the feature extraction. Comparison with traditional target detectors, such as CFAR, PCA, etc. is performed in detail. Experimental results show the superiorities of the proposal in both accuracy and efficiency.

Key words: synthetic aperture radar, targets of interest, effective feature, target discrimination

曾丽娜;周德云;邢孟道;张堃. 一种多特征联合的地面SAR目标分层检测方法[J]. 西安电子科技大学学报, 2016, 43(2): 89-94.

ZENG Lina;ZHOU Deyun;XING Mengdao;ZHANG Kun. Novel SAR target detection algorithm via multiple features[J]. Journal of Xidian University, 2016, 43(2): 89-94.

参考文献

［1］林旭, 洪峻, 孙显, 等. 一种基于自适应背景杂波模型的宽幅SAR图像CFAR舰船检测算法［J］. 遥感技术与应用, 2014, 29(1): 75-81.
LIN Xu, HONG Jun, SUN Xian, et al. New CFAR Ship Detection Algorithm Based on Adaptive Background Clutter Model in Wide Swatch SAR Images ［J］. Remote Sensing Technology and Application, 2014, 29(1): 75-81.
［2］ LIU S, CAO Z J. SAR Image Target Detection in Complex Environments Based on Improved Visual Attention Algorithm ［J］. EURASIP Journal on Wireless Communications and Networking, 2014, 2014(1): 54.
［3］刘冬, 张弓. 基于指数小波分形特征的SAR图像特定目标检测［J］. 西安电子科技大学学报, 2010, 37(2): 366-373.
LIU Dong, ZHANG Gong. Special Target Detection of the SAR Image via Exponential Wavelet Fractal［J］. Journal of Xidian University, 2010, 37(2): 366-373.
［4］赵明波, 何峻, 付强. SAR图像CFAR检测的快速算法综述［J］. 自动化学报, 2012, 38(12): 1885-1895.
ZHAO Mingbo, HE Jun, FU Qiang. Survey on Fast CFAR Detection Algorithms for SAR Image Targets ［J］. Acta Automatica Sinica, 2012, 38(12): 1885-1895.
［5］李开明, 张群, 罗迎. 地面车辆目标识别研究综述［J］. 电子学报, 2014, 42(3): 538-546.
LI Kaiming, ZHANG Qun, LUO Ying. Review of Ground Vehicles Recognition ［J］. Acta Electronica Sinica, 2014, 42(3): 538-546.
［6］ NOVAK L M, HESSE S R. Optimal Polarization for Radar Detection and Recognition of Targets in Clutter ［C］//Proceedings of SPIE: 1700. Bellingham: SPIE, 1992: 114-118.
［7］ WANG C, JIANG S F, ZHANG H. Ship Detection for High-resolution SAR Images Based on Feature Analysis［J］. IEEE Geoscience and Remote Sensing Letters, 2014, 11(1): 119-123.
［8］ WANG W Q, WANG W H. Face Feature Optimization Based on PCA ［J］. Journal of Convergence Information Technology, 2013, 8(8): 693-700.
［9］高贵, 蒋咏梅, 张琦, 等. 基于多特征联合的高分辨率SAR图像机动目标快速获取［J］. 电子学报, 2006, 34(9): 1663-1667.
GAO Gui, JIANG Yongmei, ZHANG Qi, et al. Fast Acquirement of Vehicle Targets from High-resolution SAR Images Based on Combing Multi-feature ［J］. Acta Electronica Sinica, 2006, 34(9): 1663-1667.
［10］ BHATTACHARYA J, SANYAL G, MAJUMDER S. A Robust Biometric Image Texture Descripting Approach ［J］. International Journal of Computer Applications, 2012, 53(3): 30-36.
［11］ GUPTA B, GUPTA S, TIWARI A K. Face Detection Using Gabor Feature Extraction And Artificial Neural Network［EB/OL］. ［2014-10-26］. http:///wenku.baidu.com/view/1dffb7150b4e767f5a.
［12］ EL-SAID S A. Reliable Face Recognition Using Artificial Neural Network ［J］. International Journal of System Dynamics Applications, 2013, 2(2): 14-42.
［13］艾加秋, 齐向阳, 禹卫东. 改进的SAR图像双参数CFAR舰船检测算法［J］. 电子与信息学报, 2009, 31(12): 2881-2885.
AI Jiaqiu, QI Xiangyang, YU Weidong. Improved Two Parameter CFAR Ship Detection Algorithm in SAR Images ［J］. Journal of Electronics ＆ Information Technology, 2009, 31(12): 2881-2885.

[1]	王昕,常贵清,池琛琛. 广角SAR各向异性散射目标分离成像[J]. 西安电子科技大学学报, 2021, 48(2): 109-116.
[2]	陆金文,闫华,殷红成,张磊,董纯柱. 用于三维散射中心SBR建模的边缘绕射修正[J]. 西安电子科技大学学报, 2021, 48(2): 117-124.
[3]	陶毅,丁丽. 利用共轭对称性的数字IQ频域校准方法[J]. 西安电子科技大学学报, 2021, 48(2): 181-189.
[4]	胡利平,闫华,钟卫军,殷红成,王超. 舰船目标三维散射中心建模及SAR快速仿真方法[J]. 西安电子科技大学学报, 2021, 48(2): 72-83.
[5]	段崇棣,韩超垒,杨志伟,张庆君. 一种杂波分类辅助的近海岸模糊杂波抑制方法[J]. 西安电子科技大学学报, 2021, 48(2): 64-71.
[6]	杨军,周芳. 分布式小卫星MIMO-SAR超高分辨成像方法[J]. 西安电子科技大学学报, 2021, 48(2): 99-108.
[7]	王志浩,张金松,孙光才,李军,邢孟道. 一种联合多特征量的SAR人造微弱痕迹检测方法[J]. 西安电子科技大学学报, 2019, 46(6): 131-139.
[8]	杨磊,岳云泽,李埔丞,章涛,杨桓. 多地面运动目标大动态SAR成像稀疏表示[J]. 西安电子科技大学学报, 2019, 46(5): 31-40.
[9]	郭睿,臧博,彭树铭,邢孟道. 高分辨InSAR中的城市高层建筑特征提取[J]. 西安电子科技大学学报, 2019, 46(4): 137-143.
[10]	曾创展,朱卫纲,贾鑫. 一种稀疏孔径逆合成孔径雷达成像算法[J]. 西安电子科技大学学报, 2019, 46(3): 123-129.
[11]	梁毅, 李晴晴, 孙昆, 党彦锋, 丁金闪. 利用SAR图像匹配的弹体定位新方法[J]. 西安电子科技大学学报, 2018, 45(6): 1-6.
[12]	孙宸, 成立业. 空间感知矩阵学习的极化SAR图像分类[J]. 西安电子科技大学学报, 2018, 45(6): 92-98.
[13]	刘建歌, 慕德俊. 一种SAR影像冰水边缘线提取算法[J]. 西安电子科技大学学报, 2018, 45(6): 106-111.
[14]	王跃锟;索志勇;李真芳;张金强;张庆君. 高低轨异构双基地SAR改进CS成像算法[J]. 西安电子科技大学学报, 2018, 45(5): 50-56+196.
[15]	许强;李伟;占荣辉;邹鲲. 一种改进的卷积神经网络SAR目标识别算法[J]. 西安电子科技大学学报, 2018, 45(5): 177-183.

一种多特征联合的地面SAR目标分层检测方法

Novel SAR target detection algorithm via multiple features

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10