基于Contourlet变换和均值漂移的SAR图像分割

SAR Images Segmentation Algorithm Using Mean Shift on Contourlet Domain

基于斑噪特性和纹理特征,提出了一种完全无监督的SAR图像分割算法。针对SAR图像的Contourlet变换,提出了子带选取的能量标准,对选定的子带计算能量特征和共生特征;依据特征向量的相似度剔除相近特征向量,用均值漂移算法获取纹理区域数和相应的中心特征,用像素的特征向量与相应中心特征向量的距离确定它们的分类。该文提出的方法不需要先验知识和训练样本。实验表明,基于Contourlet变换的均值漂移分割算法对混合Brodatz图像和SAR图像的分割取得了满意结果。

This paper presents an unsupervised texture-based segmentation algorithm which uses reduced Contourlet transform sub-bands and mean shift clustering, to analyze the texture information of high resolution SAR images. Two steps and criteria are proposed to reduce the sub-bands and dimension of the feature space. The mean shift clustering method is used to obtain the number of texture regions and the centre of the label class. Group the pixels into corresponding texture region by their simple distance to the class centre pixel. Experiments on a mixture of Brodatz texture and SAR images show the proposed algorithm of using Contourlet transform and mean shift clustering gives satisfactory results.