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基于MPM准则的无监督SAR图像分割 被引量：4

MPM-based Unsupervised Segmentation Method for SAR Images
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摘要 提出了一种基于MPM(maximizationoftheposteriormarginals)准则的SAR图像无监督分割方法 。 An unsupervised segmentation method for synthetic aperture radar (SAR) images is proposed, based on the maximization of the posterior marginals (MPM). Results for simulated and true SAR images are given.
作者 曹永锋 孙洪 杨文 徐新
机构地区 武汉大学电子信息学院
出处 《武汉大学学报（信息科学版）》 EI CSCD 北大核心 2004年第9期812-815,821,共5页 Geomatics and Information Science of Wuhan University
基金 国家自然科学基金资助项目 ( 60 3 72 0 5 7 40 3 760 5 1) 湖北省自然科学基金资助项目 ( 2 0 0 2AB0 0 3 4)
关键词 无监督分割 SAR图像 MPM 马尔柯夫随机场 unsupervised image segmentation synthetic aperture radar images maximization of the posterior marginals Markov random field
分类号 TP751 [自动化与计算机技术—检测技术与自动化装置]
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参考文献7

  • 1Comer M L, Delp E J. The EM/MPM Algorithm for Segmentation of Textured Images: Analysis and Further Experimental Results. The IEEE International Conference on Image Processing, Lausanne, Switzerland, 1996
  • 2Zhang J, Modestino L J W D A. Maximum-like- lihood Parameter Estimation for Unsupervised Stochastic Model-based Image Segmentation. IEEE Trans. on Image Processing, 1994,3(4):404～420
  • 3Melas D, Wilson S. Double Markov Random Fields and Bayesian Image Segmentation. IEEE Trans. on Signal Processing, 2002. 357～365
  • 4Li S Z. Markov Random Field Modeling in Computer Vision. New York, Berlin, Heidelberg, Tokyo:Springer-Verlag,1995
  • 5Oliver C, Quegan S. Understanding Synthetic Aperture Radar Images. Norwood:Artech House Inc, 1998
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同被引文献27

  • 1汤凌,郑肇葆,虞欣.一种基于人工免疫的图像分割算法[J].武汉大学学报（信息科学版）,2007,32(1):67-70. 被引量：16
  • 2Schumacher R,Schiller J. Non-cooperative Target Identification of Battlefield Targets Classification Re suits Based on SAR Images[C]. IEEE International Radar Conference,Virginia, USA, 2005.
  • 3Saghri J A, Andrew D. Exploitation of Target Shadows in Synthetic Aperture Radar Imagery for Automatic Target Recognition[J]. SHE, 2006 ( 6 312): 1-11.
  • 4Cui Jingjing, Gudnason J, Brookes M. Radar Shadow and Superresolution Features for Automatic Recognition of MSTAR Targets[C]. IEEE International Radar Conference,2005, 5(18/23) :589-592.
  • 5Papson S. The Exploitation of Multi look Synthetic Aperture Radar and Inverse Synthetic Aperture Ra dar Images for Non-cooperative Target Recognition [D]. Pennsylvania: The Pennsylvania State University,2007.
  • 6Bicego M, Murino V. 2D Shape Recognition by Hid den Markov Models [C]. The 11th International Conference on Image Analysis and Processing, Palermo, Italy, 2001.
  • 7Bicego M, Murino V. Investigating Hidden Markov Models Capabilities in 2D Shape Classification[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2004,26 : 281-286.
  • 8Cai Jinhai, Liu Zhiqiang. Hidden Markov Models with Spectral Features for 2D Shape Recognition [J].IEEE Transaction on Pattern Analysis and Machine Intelligence, 2001,23 : 1 454-1 458.
  • 9Albrecht T W,Gustafson S C. Hidden Markov Models for Classifying SAR Targets Images [J]. SHE, 2004(5 427):302 -308.
  • 10Pei Bingnan, Bao Zheng. Radar Target Recognition Based on Peak Location of HRR Profile and HMMs Classifiers[C]. IEEE International Radar Conference,Edinburgh, UK, 2002.

引证文献4

二级引证文献34

武汉大学学报（信息科学版）
2004年 第9期

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