ABSTRACT

In this paper, we propose a new algorithm using a conditional random field (CRF) model based on texture features for Synthetic Aperture Radar (SAR) image segmentation. Using the benefit of contourlet transform in describing the texture of SAR image, first, we extract contourlet coefficients from the image. Then, to take advantage of the simultaneous use of low- and high-frequency contourlet subbands, we apply the mixture of generalized Gaussian model (MoGGM) on all contourlet sub-bands to obtain more accurate texture features. Suggesting a new unary potential function based on MoGGM in our proposed CRF, we no longer require estimating the parameters in the multinominal logistic regression (MLR) model in previous CRF methods. Furthermore, we utilize the fifth-order moment to estimate the parameters of MoGGM for achieving better statistical properties of a specific region in SAR images and improving the accuracy of the texture recognition process. The experimental analysis demonstrates that segmentation results using the proposed algorithm are effectively improved compared to the previous CRF methods for SAR image segmentation.

摘要

在本文中，我们提出了一种基于纹理特征的条件随机场（CRF）模型用于合成孔径雷达（SAR）图像分割的新算法。利用Contourlet变换的优势来描述SAR图像的纹理，首先，我们从图像中提取Contourlet系数。然后，为了充分利用低频和高频轮廓波子带的优势，我们在所有轮廓波子带上应用了广义高斯模型（MoGGM）的混合，以获得更准确的纹理特征。在我们提出的CRF中建议基于MoGGM的新的一元势函数，我们不再需要在以前的CRF方法中估计多项式Lo​​gistic回归（MLR）模型中的参数。此外，我们利用五阶矩来估计MoGGM的参数，以实现SAR图像中特定区域的更好统计特性，并提高纹理识别过程的准确性。实验分析表明，与以前的CRF SAR图像分割方法相比，该算法的分割结果得到了有效的改善。