期刊文献+

结合目标先验信息的检测图像网格聚类算法 被引量:2

grid clustering in detection image via target prior information
下载PDF
导出
摘要 合成孔径雷达(Synthetic Aperture Radar,SAR)图像自动目标识别的前提条件之一是能够准确地提取感兴趣区域(Region of interest,ROI),因此能够获取ROI中心的聚类算法是SAR图像处理的关键算法之一。为了尽可能降低检测图像中的虚警以及减少聚类及相应的鉴别算法的计算量,本文提出一种基于先验信息的网格聚类算法,该方法首先通过目标和杂波的形状统计信息估计网格聚类参数,然后利用其对检测图像进行网格划分,并引入目标的占空比特征去除杂波,最后通过粗提取和精提取两种方法计算得到聚类中心。仿真和实测数据处理结果表明,该算法能够对检测目标进行有效聚类并去除大部分杂波,同时极大地减少了鉴别的计算量,且简化了传统ROI中心提取流程。 For synthetic aperture radar (SAR) images, the key to successful automatic target recognition is accurate extraction of regions of interests (ROI), and as a consequence, the clustering algorithm is the key to ROI extraction. A novel approach based on grid clustering is proposed in this paper, which can significantly reduce the false alarm rate and the computational complexity. Firstly, the statistics of geometrical features are gathered to determine the parameters of the algorithm; then, the whole duty ratio map is generated by computing the duty ratio in each grid and the oversized and undersized clutters is discarded according to the corresponding duty ratios ; at last, the ROI centers are estimated from the duty ratio map. The feasibility and effectiveness of our proposed approach are demonstrated by simulation and real experimental results.
出处 《信号处理》 CSCD 北大核心 2012年第11期1565-1574,共10页 Journal of Signal Processing
基金 国家自然科学基金项目(61271441 60972121) 全国优秀博士学位论文作者专项资金资助项目(201046) 新世纪优秀人才支持计划资助项目(NCET-10-0895) 国防科技大学科研计划项目(CJ12-04-02)
关键词 先验信息 网格聚类 时间复杂度 Prior Information Grid Clustering Time Complexity
  • 相关文献

参考文献13

  • 1Gao G,Kuang G Y,Zhang Q,et al: Fast detecting and lo- cating groups of targets in high-resolution SAR images [ J]. Pattern Recognition ,2007,40 : 1378-1384.
  • 2Jin T,Zhou Z M. Feature Extraction and Discriminator De- sign for Landmine Detection on Double-Hump Signature in Uhrawideband SAR [ J]. IEEE Tranactions on Geoscience And Remote Sensing, 2008,46 ( 11 ) : 3783-3791.
  • 3Shi Y F, Jin T, Song Q, et al: A segmentation-based CFAR algorithm for subsurface targets detection in FLGP- VAR[ C]. 2010 2nd International Conference on Signal Processing Systems, Dalian, China, 5-7 July, 2010,2 : 293- 298.
  • 4Moldovanu S, Moraru L. Mass detection and classification in breast ultrasound image using K-means clustering algo- rithm [ C]. 2010 5rd International symposium on ISEEE, Galati, Romania,2010,197-200.
  • 5Hartmann B, Banfer O,Nelles O, et al: Supervised Hierar- chical Clustering in Fuzzy Model Identification [ J ]. IEEE Transactions on Fuzzy System, 2011,19 (6) : 1 163-1176.
  • 6Whelan M, Nhien-An Le-Khac and Kechadi M. Compa- ring two density-based clustering methods for reducing very large spatio-temporal dataset [ C ]. IEEE Internation- al Conference on [CSDM 2011,2011,519-524.
  • 7Zhong Y, Yamarki H and Takakura H. A grid-based clus- tering for low-overhead anomaly intrusion detection [ C ]. 2011 5th International Conference on Network and System Security, Milan,Italy,2011,17-24.
  • 8Chu Y H. Chen Y J,Yang D N,et al: Reducing Re- dundancy in Subspace Clustering [ J]. IEEE Transac- tions on Knowledge and Data Engineering, 2009,21 (10) :1432-1446.
  • 9Tasdemir K, Merenyi E. A validity Index for Prototype- Based Clustering of Data Sets With Complex Cluster Structures [ J]. IEEE Transactions on Systems, Man and Cybernetics-Part B : Cybernetics, 2011,41 ( 4 ) : 1039-1053.
  • 10Ripon K S N, Siddique M N H. Evolutional multi-objective clustering for overlapping clusters detection [ C]. IEEE Congresss on Evolutionary Computation,2009,976-982.

二级参考文献3

同被引文献32

  • 1MOLi WUSiliang LIHai.Radar Detection of Range Migrated Weak Target Through Long-Term Integration[J].Chinese Journal of Electronics,2003,12(4):539-544. 被引量:27
  • 2AMIN M G.Through-the-wall radar imaging[M].New York:CRC Press,2011.
  • 3AFTANAS M.Through wall imaging with UWB radar system[M].Saarbrucken:Lap Lambert Academic Publishing,2009.
  • 4TAHMOUSH D,SILVIOUS J,BENDER B.Radar surveillance in urban environments[C]//2012 IEEE Radar Conference.Atlanta:IEEE,2012:220-225.
  • 5GENNARELLI G,SOLDOVIERI F.A linear inverse scattering algorithm for radar imaging in multipath environments[J].IEEE Geoscience and Remote Sensing Letters,2013,10(5):1085-1089.
  • 6SETLUR P,AMIN M,AHMAD F.Multipath model and exploitation in through-the-wall and urban radar sensing[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(10):4021-4034.
  • 7LU B Y,SONG Q,ZHOU Z M,et al.A SFCW radar for through wall imaging and motion detection[C]//2011 the 8th European Radar Conference.Manchester:IEEE,2011:325-328.
  • 8SMITH GE,MOBASSERI B G.Analysis and exploitation of multipath ghosts in radar target image classification[J].IEEE Transactions on Image Processing,2014,23(4):1581-1592.
  • 9LU B Y,SUN X,ZHAO Y,et al.Phase coherence factor for mitigation of sidelobe artifacts in through-the-wall radar imaging[J].Journal of Electromagnetic Waves and Applications,2013,27(6):716-725.
  • 10CAMACHO J,PARRILLA M,FRITSCH C.Grating-lobes reduction by application of phase coherence factors[C]//Proceedings of 2009 IEEE International Symposium on Ultrasonics.Rome:IEEE,2009:341-344.

引证文献2

二级引证文献7

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部