Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model

Wang F.; Wu Y.; Zhang Q.; Zhang P.; Li M.; Lu Y.

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Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model

机译：使用三重态马尔可夫场模型的SAR图像无监督变化检测

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摘要

The triplet Markov field (TMF) model is powerful in the nonstationary synthetic aperture radar (SAR) image analysis. Taking the speckle noise and the correlation of nonstationarities in two multitemporal SAR images into account, we propose a change-detection method based on the TMF model in this letter. The third field $U$ in the TMF model is redefined to describe the nonstationary textural similarity between the two images for change detection. The corresponding prior energy of $(X, U)$ is reconstructed. The adaptive weight parameter in prior energy is introduced to cope with the detection tradeoff issue. An automatic estimation of the parameter is obtained with low level of complexity. The Bayesian maximum posterior marginal criterion is utilized with the TMF model to obtain change detection. Experimental results on real SAR images validate the superiority of the proposed TMF method over the Markov random field method.

机译：三重态马尔可夫场（TMF）模型在非平稳合成孔径雷达（SAR）图像分析中功能强大。考虑到两个多时相SAR图像中的斑点噪声和非平稳性的相关性，本文提出了一种基于TMF模型的变化检测方法。重新定义TMF模型中的第三个字段$ U $来描述两个用于变化检测的图像之间的非平稳纹理相似性。重建相应的先验能量$（X，U）$。引入先验能量中的自适应权重参数以应对检测权衡问题。以低复杂度获得参数的自动估计。贝叶斯最大后验边缘准则与TMF模型一起使用以获得变化检测。在真实SAR图像上的实验结果验证了所提出的TMF方法优于马尔可夫随机场方法的优越性。

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来源
《Geoscience and Remote Sensing Letters, IEEE》 |2013年第4期|p.697-701|共5页
作者
Wang F.; Wu Y.; Zhang Q.; Zhang P.; Li M.; Lu Y.;
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作者单位

Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University, Xi'an, China;

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正文语种 eng
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关键词
Nonstationary anisotropic model; SAR image change detection; synthetic aperture radar (SAR); triplet Markov field model;

机译：非平稳各向异性模型;SAR图像变化检测;合成孔径雷达（SAR）;三重马尔可夫场模型;

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专利

1. Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty [J] . Yan Wu, Ming Li, Peng Zhang, Pattern recognition letters . 2011,第11期

机译：基于边缘罚分的三重态马尔可夫场模型对SAR图像进行无监督的多类分割
2. Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model [J] . Zhang P., Li M., Wu Y., Pattern Recognition: The Journal of the Pattern Recognition Society . 2012,第11期

机译：模糊三重态马尔可夫场模型的无监督SAR图像多类分割
3. Unsupervised SAR Image Segmentation Using Ambiguity Label Information Fusion in Triplet Markov Fields Model [J] . Fan Wang, Yan Wu, Peng Zhang, IEEE Geoscience and Remote Sensing Letters . 2017,第9期

机译：三重态马尔可夫场模型中使用歧义标签信息融合的无监督SAR图像分割
4. Unsupervised SAR image segmentation using gradient Triplet Markov fields model [C] . Fan Wang, Yan Wu, Peng Zhang, Asia-Pacific Conference on Synthetic Aperture Radar . 2015

机译：使用梯度三重态马尔可夫场模型的无监督SAR图像分割
5. Unsupervised segmentation of noisy and textured images modeled by Markov random fields. [D] . Gregoriou, Georghios Kyriacos. 1992

机译：由Markov随机场建模的噪点和纹理图像的无监督分割。
6. Unsupervised Cerebrovascular Segmentation of TOF-MRA Images Based on Deep Neural Network and Hidden Markov Random Field Model [O] . Shengyu Fan, Yueyan Bian, Hao Chen, 2019

机译：基于深度神经网络和隐马尔可夫随机场模型的TOF-MRA图像无监督脑血管分割
7. Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions [O] . Dalila Benboudjema, Florence Tupin, Wojciech Pieczynski, 2012

机译：利用三重马尔可夫场和Fisher噪声分布对saR图像进行无监督分割

Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model

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