摄影测量学与遥感

稀疏场景目标的距离像峰值聚类分割成像方法

  • 杨秋 ,
  • 张群 ,
  • 王敏 ,
  • 孙莉
展开
  • 空军工程大学信息与导航学院, 陕西 西安 710077
杨秋(1986-),男,博士生,研究方向为雷达信号处理。E-mail:yangqiu1105@163.com

收稿日期: 2014-06-16

  修回日期: 2015-01-27

  网络出版日期: 2015-09-02

基金资助

国家自然科学基金(61172169);陕西省自然科学基金(2013JQ8027)

Targets Separation and Imaging Method in Sparse Scene Based on Cluster Result of Range Profile Peaks

  • YANG Qiu ,
  • ZHANG Qun ,
  • WANG Min ,
  • SUN Li
Expand
  • Information and Navigation College, Air Force Engineering University, Xi'an 710077, China

Received date: 2014-06-16

  Revised date: 2015-01-27

  Online published: 2015-09-02

Supported by

The National Natural Science Foundation of China(No.61172169);The National Science Foundation Research Program of Shaanxi Province(No.2013JQ8027)

摘要

针对海面舰船等具有一定空间稀疏性的合成孔径雷达成像场景,提出了一种稀疏场景目标的距离像峰值聚类分割成像方法。首先采用小波降噪算法对SAR原始回波数据进行预处理,通过距离压缩和距离徙动校正获得不同观测位置的距离像,利用基于二阶差分算子的快速峰值检测算法检测距离像峰值点,对峰值检测结果距离维聚类后方位向成像,实现了距离向能量区间稀疏目标的分割成像;对峰值检测结果距离-方位二维聚类后方位向成像,实现了距离向能量区间与方位向合成孔径时间无耦合稀疏目标的分割成像。仿真结果表明,对海面舰船等具有空间稀疏性的成像场景,所提方法能够实现目标的有效分割成像,不仅在完整保留目标回波信息的同时大幅度地降低了方位向压缩的运算量,而且分割成像结果更有利于目标的快速识别。

本文引用格式

杨秋 , 张群 , 王敏 , 孙莉 . 稀疏场景目标的距离像峰值聚类分割成像方法[J]. 测绘学报, 2015 , 44(8) : 900 -908 . DOI: 10.11947/j.AGCS.2015.20140310

Abstract

This paper focuses on the synthetic aperture radar (SAR) imaging of space-sparse targets such as ships on the sea, and proposes a method of targets separation and imaging of sparse scene based on cluster result of range profile peaks. Firstly, wavelet de-noising algorithm is used to preprocess the original echo, and then the range profile at different viewing positions can be obtained by range compression and range migration correction. Peaks of the range profiles can be detected by the fast peak detection algorithm based on second order difference operator. Targets with sparse energy intervals can be imaged through azimuth compression after clustering of peaks in range dimension. What's more, targets without coupling in range energy interval and direction synthetic aperture time can be imaged through azimuth compression after clustering of peaks both in range and direction dimension. Lastly, the effectiveness of the proposed method is validated by simulations. Results of experiment demonstrate that space-sparse targets such as ships can be imaged separately and completely with a small computation in azimuth compression, and the images are more beneficial for target recognition.

参考文献

[1] NI Chong, WANG Yanfei, XU Xianghui, et al. An Improved Autofocus Algorithm for SAR Images[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(3): 449-453. (倪崇, 王岩飞, 徐向辉, 等. 一种改进的SAR图像聚焦算法[J]. 测绘学报, 2012, 41(3): 449-453.)
[2] LI Chunsheng, YANG Wei, WANG Pengbo. A Review of Spaceborne SAR Algorithm for Image Formation[J]. Journal of Radars, 2013, 2(1): 111-122. (李春升, 杨威, 王鹏波. 星载SAR成像处理算法综述[J]. 雷达学报, 2013, 2(1): 111-122.)
[3] LIU Jixin, SUN Quansen. Multi-scale Fractal Compressed Sensing Remote Sensing Imaging[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(6): 846-852. (刘佶鑫, 孙权森. 多尺度分形压缩感知遥感成像方法[J]. 测绘学报, 2013, 42(6): 846-852.)
[4] JIAO Licheng, ZHANG Xiangrong, HOU Biao, et al. Intelligent SAR Image Processing and Interpretation[M]. Beijing: Science Press, 2008. (焦李成, 张向荣, 侯彪, 等. 智能SAR图像处理与解译[M]. 北京: 科学出版社, 2008.)
[5] HU Zhaoling. An Unsupervised Change Detection Approach Based on KI Dual Thresholds under the Generalized Gauss Model Assumption in SAR Images[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 116-122. (胡召玲. 广义高斯模型及KI双阈值法的SAR图像非监督变化检测[J]. 测绘学报, 2013, 42(1): 116-122.)
[6] WU Yan, JIAO Jingmei, YANG Xiaoli, et al. Segmentation Algorithm for SAR Images Based on Fusion of HMT in the Contourlet Domain and D-S Theory of Evidence[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(2): 148-155. (吴艳, 焦惊眉, 杨晓丽, 等. 基于Contourlet域HMT和D-S证据融合的SAR图像分割[J]. 测绘学报, 2011, 40(2): 148-155.)
[7] WU Zhaocong, HU Zhongwen, ZHANG Qian, et al. On Combining Spectral, Textural and Shape Features for Remote Sensing Image Segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 44-50. (巫兆聪, 胡忠文, 张谦, 等. 结合光谱、纹理与形状结构信息的遥感影像分割方法[J]. 测绘学报, 2013, 42(1): 44-50.)
[8] ZHAO Bei, ZHONG Yanfei, ZHANG Liangpei. High Spatial Resolution Remote Sensing Image Segmentation Based on Multi-agent Theory[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(1): 108-115. (赵贝, 钟燕飞, 张良培. 高空间分辨率遥感影像的多智能体分割方法研究[J]. 测绘学报, 2013, 42(1): 108-115.)
[9] FU Yusheng, CAO Zongjie, PI Yiming. Multi-Region Segmentation of SAR Image by a Multiphase Level Set Approach[J]. Journal of Electronics (China). 2008, 25(4): 556-561.
[10] SINGLY J, DATCU M. Automated Interpretation of Very-high Resolution SAR Images[C]//IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Munich: IEEE, 2012: 3724-3727.
[11] BAO Yansong, WANG Junzhan, ZHANG Youjing, et al. A Semi-empirical Model for Correction of Terrain Influences in SAR Backscattering[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4): 483-488. (鲍艳松, 王军战, 张友静, 等. 合成孔径雷达影像地形校正半经验模型[J]. 测绘学报, 2011, 40(4): 483-488.)
[12] LI Yu, JI Kefeng, SU Yi. Surveys on SAR Image Segmentation Algorithms[J]. Journal of Astronautics, 2008, 29(2): 407-412. (李禹, 计科锋, 粟毅. 合成孔径雷达图像分割技术综述[J]. 宇航学报, 2008, 29(2): 407-412.)
[13] WU Yan, WANG Xin, XIAO Ping, et al. Fast Algorithm Based on Triplet Markov Fields for Unsupervised Multi-class Segmentation of SAR Images[J]. Science China Information Sciences, 2011, 54(7): 1524-1533.
[14] FANG Ning, TAN Fei. A Fast Target Detection Method for SAR Image[C]//IEEE 12th International Conference on Computer and Information Technology. Chengdu: IEEE, 2012: 778-781.
[15] WAN Honglin, JIAO Licheng, WANG Guiting, et al. A Region-of-interest Level Method for Change Detection in SAR Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 239-245. (万红林, 焦李成, 王桂婷, 等. 在感兴趣的区域层面上进行SAR图像变化检测的方法研究[J]. 测绘学报, 2012, 41(2): 239-245.)
[16] YUE Chunyu, JIANG Wanshou. An Algorithm of SAR Image Denoising in Nonsubsampled Contourlet Transform Domain Based on Maximum A Posteriori and Non-local Restriction[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(1): 59-64. (岳春宇, 江万寿. 基于最大后验和非局域约束的非下采样轮廓波变换域SAR图像去噪方法[J]. 测绘学报, 2012, 41(1): 59-64.)
[17] HOU Yinghui, XU Rongqing. Study on Denoising Algorithm Based on Wavelet Transform for Weak Space-borne Radar Target[J]. Journal of Natural Science of Heilongjiang University, 2006, 23(5): 696-699. (侯颖辉, 许荣庆. 星载雷达弱目标小波去噪方法研究[J]. 黑龙江大学自然科学学报, 2006, 23(5): 696-699.)
[18] ZHANG Weiqiang, SONG Guoxiang. Signal De-noising in Wavelet Domain Based on a New Kind of Thresholding Function[J]. Journal of Xidian University, 2004, 31(2): 296-303. (张维强, 宋国乡. 基于一种新的阈值函数的小波域信号去噪[J]. 西安电子科技大学学报: 自然科学版, 2004, 31(2): 296-303.)
[19] CHEN Baolin. Theory and Method of Optimization[M]. Beijing: Tsinghua University Press, 2012. (陈宝林. 最优化理论与算法[M]. 北京: 清华大学出版社, 2012.)
[20] LIU Yiming, ZHANG Huaxiang. New Hierarchical Clustering Method Using Information Gain[J]. Computer Engineering and Applications, 2012, 48(1): 142-144. (刘一鸣, 张化祥. 引入信息增益的层次聚类算法[J]. 计算机工程与应用, 2012, 48(1): 142-144.)
[21] ZHANG Shuai, ZHONG Yanfei, ZHANG Liangpei. An Automatic Fuzzy Clustering Algorithm Based on Self-adaptive Differential Evolution for Remote Sensing Image[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(2): 239-246. (张帅, 钟燕飞, 张良培. 自适应差分进化的遥感影像自动模糊聚类方法[J]. 测绘学报, 2013, 42(2): 239-246.)
[22] LIU Qiliang, DENG Min, SHI Yan, et al. A Novel Spatial Clustering Method Based on Multi-Constraints[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4): 509-516. (刘启亮, 邓敏, 石岩, 等. 一种基于多约束的空间聚类方法[J]. 测绘学报, 2011, 40(4): 509-516.)
文章导航

/