CASIA OpenIR  > 09年以前成果
Spatially variant mixture multiscale autoregressive Modeling of SAR imagery for unsupervised segmentation
Ju, YW; Tian, Z
Source PublicationCHINESE JOURNAL OF ELECTRONICS
2006-04-01
Volume15Issue:2Pages:359-362
SubtypeArticle
AbstractA new and efficient Spatially variant mixture multiscale autoregressive (SVMMAR) model is presented for unsupervised segmentation of Synthetic aperture radar (SAR) imagery. Unlike previously proposed Bayesian segmentation approaches based on mixture model, which have used a single-resolution representation of the observed image, the proposed model can be used for the pyramid representation of SAR imagery. So, it is capable of not only describing spatially variant characteristics and complexity, but also characterizing and exploiting multiscale stochastic structure inherent in SAR imagery due to radar speckle. The estimation of parameters of the model is easily performed via Least square (LS) estimation and Expectation maximization (EM) algorithm. Then, the proposed algorithm yields maximum likelihood estimates of the labels themselves and results in unambiguous pixel labels. Moreover, a kind of method for selecting number of classes at a coarser scale is proposed, which reduced computation amount greatly. The advantage of our proposed segmentation approach is that it is not only fast, able to automatically estimate all the model parameters, and easy to implement, but also obviates the need for an explicit labeling rule and speckle reduction preprocessing before segmentation. Therefore, the model can be exploited for SAR Automatic target recognition (ATR). All of that are demonstrated by the experimental results.
KeywordSpatially Variant Mixture Multiscale Autoregressive Model Least Square Estimation Em (Expectation Maximization) Algorithm Unsupervised Segmentation Sar (Synthetic Aperture Radar) Imagery
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000236935300039
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9337
Collection09年以前成果
Affiliation1.Northwestern Polytech Univ, Dept Appl Math, Xian 710072, Peoples R China
2.Chinese Acad Sci, Natl Key Lab Pattern Recognit, Beijing 100080, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Ju, YW,Tian, Z. Spatially variant mixture multiscale autoregressive Modeling of SAR imagery for unsupervised segmentation[J]. CHINESE JOURNAL OF ELECTRONICS,2006,15(2):359-362.
APA Ju, YW,&Tian, Z.(2006).Spatially variant mixture multiscale autoregressive Modeling of SAR imagery for unsupervised segmentation.CHINESE JOURNAL OF ELECTRONICS,15(2),359-362.
MLA Ju, YW,et al."Spatially variant mixture multiscale autoregressive Modeling of SAR imagery for unsupervised segmentation".CHINESE JOURNAL OF ELECTRONICS 15.2(2006):359-362.
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