区域GMM聚类的SAR图像分割

SAR image segmentation with region-based GMM

高斯混合模型(GMM)聚类算法近年来广泛应用于图像分割领域。但在SAR图像分割中,由于忽略了图像像素间的空间相关性,使其对相干斑噪声十分敏感。提出一种基于区域的GMM聚类算法,它将空间相关性引入聚类分类中,利用分水岭分割得到基本同质区域,计算区域的灰度均值作为GMM聚类算法的输入样本,将聚类特征从像素水平提升到区域水平,减少了噪声对分割结果的影响;并将自身反馈机制引入期望最大化(EM)算法中,进一步提高了GMM模型参数估计的精度。还对合成图像和真实SAR图像进行了分割实验,结果表明新算法可有效地提高分割的准确性。

Ganssian mixture model （GMM） clustering algorithm is widely used in image segmentation during recent years, The algorithm is however quite sensitive to speckle noise since spatial correlations between pixels are ignored. This paper presents a region-based GMM clustering algorithm for SAR image segmentation featured by incorporating spatial correlations. The watershed algorithm is first used to generate primitive homogeneous regions. Regional mean values are then calculated as input samples of the GMM clustering process. The impact of noise on the segmentation result can therefore be reduced in the space of regions instead of pixels. A feedback mechanism is further introduced into the expectation-maximization （EM） algorithm to improve the precision of parameter estimation. The efficiency of the proposed algorithm has been demonstrated on the segmentation of synthetic SAR images and real SAR images, where the segmentation accuracy has been substantially improved in contrast to pixel-based the GMM algorithm.