Nonsubsampled contourlet transform-based conditional random field for SAR images segmentation
Nonsubsampled contourlet transform-based conditional random field for SAR images segmentation
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DOI：
10.1016/j.sigpro.2020.107623
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发表时间：
2020-09-01
影响因子：
4.4
通讯作者：
Danyali, Habibollah
中科院分区：
文献类型：
--
作者：
Golpardaz, Maryam;Helfroush, Mohammad Sadegh;Danyali, Habibollah
本文提出了一种新的基于纹理的条件随机场（CRF）算法用于合成孔径雷达（SAR）图像分割。在我们提出的算法中，为了克服基于强度的特征的局限性，在contourlet变换域中进行特征提取。我们将非下采样轮廓波变换（NSCT）作为一种补偿传统轮廓波不足的过完备变换。采用广义高斯分布（GGD）对NSCT系数进行统计描述，同时在条件随机场模型中提取SAR图像的适当统计量，克服了基于强度特征的散斑效应。这样，不仅不需要在一元函数中考虑一个额外的项来对SAR图像进行统计建模，而且也不再需要计算基于斑点灰度直方图的几个准则。实验结果表明，与其他基于变换的特征（如小波）相比，NSCT具有优势，并且与基于CRF模型中强度的方案相比，NSCT的精度也有所提高。(C) 2020 Elsevier B.V.版权所有
In this paper, we propose a new texture-based conditional random field (CRF) for Synthetic Aperture Radar (SAR) image segmentation. In our proposed algorithm to overcome the limitations of the intensitybased features, feature extraction is performed in the contourlet transform domain. We use the nonsubsampled contourlet transform (NSCT) as an overcomplete transform which compensates the shortcomings of the traditional contourlet. Applying the generalized Gaussian distribution (GGD) for the statistical description of NSCT coefficients, we simultaneously extract proper statistics from SAR image in the conditional random field model and overcome the speckle effects in the intensity-based features. In this way, not only there is no need to consider an additional term in unary function to model the statistics of SAR image but also, we no longer need to calculate the several criteria based on the histogram of speckled gray levels. Experimental results show the superiority of NSCT compared to the other transformbased features such as wavelet and also demonstrate the improvement of the accuracy in contrast to the schemes which are based on the intensity in the CRF model. (C) 2020 Elsevier B.V. All rights reserved.