基于相似性的POLSAR占优散射归类及非监督聚类

POLSAR Dominant Scattering Mechanism Clustering and Unsupervised Classification Based on Similarity

极化相似性满足旋转不变性、尺度无关性及有界性,基于此,该文提出一种极化相似性与Freeman模型相结合的POLSAR占优散射归类及非监督聚类方法。实验表明,该方法解决了Freeman分解在分类应用中所存在的问题,相比于直接应用Freeman分解的分类方法,在地物散射特征的描述上更加准确。

A new POLSAR dominant scattering mechanism clustering and unsupervised classification method is proposed based on polarimetric scattering similarity and Freeman decomposition models.As polarimetric scattering similarity is roll-invariant and nonnegative,the method fixes the problems of Freeman decomposition in classification.Experiment shows that,comparing to the classification method based on Freeman decomposition,the proposed method depicts terrain scattering properties more accurately.