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

DENG Xiao-zheng; JIAO Li-cheng; LU Shan

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

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- Spectral Clustering Ensemble Applied to SAR Image Segmentation Using Nonnegative Matrix Factorization
- Acta Electronica Sinica Vol. 39, Issue 12, Pages: 2905-2909(2011)
- 作者机构：
  
  西安电子科技大学智能感知与图像理解教育部重点实验室,陕西,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Published：2011
- 稿件说明：
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DENG Xiao-zheng, JIAO Li-cheng, LU Shan. Spectral Clustering Ensemble Applied to SAR Image Segmentation Using Nonnegative Matrix Factorization[J]. Acta Electronica Sinica, 2011, 39(12): 2905-2909.
DOI：

DENG Xiao-zheng, JIAO Li-cheng, LU Shan. Spectral Clustering Ensemble Applied to SAR Image Segmentation Using Nonnegative Matrix Factorization[J]. Acta Electronica Sinica, 2011, 39(12): 2905-2909. DOI：

摘要

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.

Abstract

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.

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