基于非负矩阵分解的谱聚类集成SAR图像分割

 本文提出了一种新颖的基于非负矩阵分解的谱聚类集成SAR图像分割框架.首先,个体分割结果的产生采用基于Nystrom逼近的谱聚类方法,使用不同的尺度参数,得到具有差异性的个体分割结果;其次,使用非负矩阵分解的方法来合并这些个体分割结果,使用非负矩阵分解方法的优点在于其合乎人类大脑感知的直观体验,并具有明确的物理含义;最后,根据合并得到的像素点隶属度关系得到SAR图像分割结果.为了验证本文方法的有效性,对3幅纹理图像和4幅SAR图像进行分割实验,并对比K-means方法、基于Nystrom逼近的谱聚类方法、Meta-clustering方法,本文的方法无论是定性还是定量分析都是较好的,并具有一定的实用性.

Spectral Clustering Ensemble Applied to SAR Image Segmentation Using Nonnegative Matrix Factorization

In this paper,a novel method based on spectral clustering ensemble using nonnegative matrix factorization (NMF) is proposed for the segmentation of SAR image.Firstly,diversity segmentation components are obtained due to the spectral clustering method is sensitive to the scaling parameter.Secondly,these components are combined by using NMF,NMF is a method that can obtain a representation of data full of intuitive meaning and physical interpretation.Finally,segmentation result is obtained according to the combined result.To show the effectiveness of the novel method,experiments with three texture images and four SAR images are considered.The segmentation results are evaluated by comparing with K-means method,spectral clustering method based on Nystrom approximation and Meta-clustering method.According to the qualitative and quantitative analysis,the proposed method is effective and has some practical value.