摘要
马尔可夫随机场(MRF)在SAR图像分割中有着广泛的应用。由于合成孔径雷达(SAR)图像本身所固有的相干斑噪声的影响,传统方法很难获得准确的分割,因此提出了一种新的基于MRF(Markov Random Field)融合Gaussian-Hermite矩(GHM)的SAR图像无监督分割算法。利用Gaussian-Hermite矩的不同阶矩作为SAR图像特征得到初始分割;将得到的初始分割结果作为MRF随机场的先验模型,通过引入一个基于两成分权重参数的能量函数,利用最大后验概率(MAP)得到最终的分割结果。通过对合成图像及SAR图像分割实验结果的比较,表明了该方法在误分率、抗噪性以及视觉效果上具有更好的效果。
Markov Random Field（MRF） has lots of applications in SAR image segmentation.Because of the multiplication nature of speckle noise in SAR image,it is hard to get accurate segmentation,in this paper,a new fusion algorithm for unsupervised SAR image segmentation based on Markov Random Field（MRF） with Gaussian-Hermite Moments（GHM） is proposed.It gets initial segmentation with different orders of Gaussian-Hermite moments as SAR image features.Then,the first result can be as initial MRF model,by introducing an energy function-based weighting parameter between the two components,the accurate result is achieved by Maximum A Poteriori（MAP）.The proposed method applied in synthetic image and SAR image segmentation,and experimental results show that it performs well in miss-classification ratio,restraining the speckle noise,and performation in vision.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第31期185-188,205,共5页
Computer Engineering and Applications
