基于谱聚类的极化SAR影像分割改进算法

谱聚类的影像分割算法是一种基于点的聚类方法,其通过选用不同的特征构建相似性度量矩阵,来衡量像元间的相似性程度。在解算过程中需要计算每2个像元间的相似性度量,在处理大幅影像时,运算量大、耗时长。针对这一问题,提出了一种改进方法。首先通过均值漂移算法对极化SAR影像进行预处理,然后选取中心像元,构建相似性度量矩阵,采用归一化分割准则完成影像分割。实验结果表明,该算法分割结果优良,准确性高,有效地提高了原算法的分割效率,具有一定的实践意义。

Polarimetric SAR Image Segmentation Algorithm by Spectral Clustering

Image segmentation by spectral clustering is a clustering method based on points. It is characterized by the use of similarity measure matrixes. We usually need to calculate the similarity matrixes between every two cells, which consumes huge computation task and a lot of time when processing large images. To solve this problem, we propose an improved method. First, we use mean shift algorithm for polarimetric SAR image, and then select center pixel to construct similarity measure matrix. At last, we use the normalized segmentation rule for image segmenta-tion. Computation experiment proves that the algorithm could improve the efficiency with high accuracy and satisfac-tory result, hence is of practical significance.