Unsupervised segmentation of SAR images using triplet Markov fields and Fisher noise distributions

被引：0

作者：

Benboudjema, Dalila ^{[1
]}

Tupin, Florence ^{[1
]}

Pieczynski, Wojciech ^{[2
]}

Sigelle, Marc ^{[1
]}

Nicolas, Jean-Marie ^{[1
]}

机构：

[1] GET ENST, Dept TSI, LTCI, UMR 5141, 46 Rue Barrault, F-75013 Paris, France

[2] GET INT, Dept CITI, CNRS UMR 5157, F-91000 Evry, France

来源：

IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET | 2007年

关键词：

nonstatioanry triplet Markov field; Fisher distributions; Synthetic aperture radar (SAR) images; parameters estimation; unsupervised segmentation;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper deals with SAR data segmentation in an unsupervised way. The model we propose is a combination of the nonstationary triplet Markov field recently introduced and the Fisher distributions. The first one allows modeling the different stationarities present in a given image. The second one has the advantage that is well adapted to this kind of data. We present an original technique based on Iterative Conditional Estimation method, to estimate the parameters of the model we propose. Application examples on simulated data and real SAR images are presented as well.

引用

页码：3891 / +

页数：2

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