Remote sensing image segmentation based on KFCM and EM hybrid clustering algorithm

Min WANG; Xin ZHANG; Wei-guo YUN; Ming-fei WEI; Jing WANG

doi:10.3788/YJYXS20173212.0999

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Remote sensing image segmentation based on KFCM and EM hybrid clustering algorithm

Image Processing | 更新时间：2020-07-07

- Remote sensing image segmentation based on KFCM and EM hybrid clustering algorithm
- Chinese Journal of Liquid Crystals and Displays Vol. 32, Issue 12, Pages: 999-1005(2017)
- 作者机构：
  
  西安建筑科技大学信息与控制工程学院, 陕西西安 710055
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20173212.0999
  CLC： P2
- Received：16 May 2017，
  
  Accepted：25 August 2017，
  
  Published：05 December 2017
- 稿件说明：
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Min WANG, Xin ZHANG, Wei-guo YUN, et al. Remote sensing image segmentation based on KFCM and EM hybrid clustering algorithm[J]. Chinese journal of liquid crystals and displays, 2017, 32(12): 999-1005.
DOI：

Min WANG, Xin ZHANG, Wei-guo YUN, et al. Remote sensing image segmentation based on KFCM and EM hybrid clustering algorithm[J]. Chinese journal of liquid crystals and displays, 2017, 32(12): 999-1005. DOI： 10.3788/YJYXS20173212.0999.

摘要

针对聚类算法在应用中分割速度慢、抑制噪声能力弱等问题，本文提出一种基于核模糊C-均值（Kernel Fuzzy C-means，KFCM）和融合期望最大化（EM）算法混合聚类的遥感图像分割。首先给原始KFCM算法引入隐含变量来对像素预定义类别，然后利用EM算法评价预定义的类别是否最优，以此完成对遥感图像的聚类分割。在利用EM算法进行评价时，对KFCM引入空间邻域信息，采用惯性权重对其初始化参数进行优化增强算法效率。与传统的聚类分割方法进行比较，研究结果表明，该方法速度快、效果好、精度也能满足应用要求，具有较高的应用价值。

Abstract

Aiming at the problem that the clustering algorithm is slow in the application and weak in noise suppression

this article proposes a remote sensing image segmentation based on improved Kernel Fuzzy C-means (KFCM) and EM algorithm hybrid clustering. Firstly

the implicit variables are introduced into the original KFCM algorithm to predefine the pixels

and then the EM algorithm is used to evaluate whether the predefined categories are optimal

so as to complete the clustering of the remote sensing images. When using the EM algorithm for evaluation

the KFCM introduces the spatial neighborhood information and the inertia weight is used to optimize the initialization parameters to enhance the efficiency of the algorithm. Compared with the traditional clustering methods

the results show that the method in this article is fast and effective

and the precision can meet the application requirements and has high application value.

关键词

Keywords

references

沈毅. 高分辨率遥感图像处理若干关键技术研究[D]. 南京: 南京航空航天大学, 2012. http://cdmd.cnki.com.cn/Article/CDMD-10287-1014059777.htm

SHEN Y. Research on some key technologies of high resolution remote sensing image processing[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2012. (in Chinese)

吴一全, 沈毅, 陶飞翔.基于局部空间信息KFCM的遥感图像聚类算法[J].地球信息科学学报, 2014, 16(5):769-775.

WU Y Q, SHEN Y, TAO F X. An algorithm of remote sensing image clustering based on kernel fuzzy C-means with local spatial information[J]. Journal of Geo-Information Science, 2014, 16(5):769-775. (in Chinese)

李晓冰.基于自适应模糊加权指数的FCM聚类测量图像分割方法[J].红外技术, 2013, 35(3):146-149.

LI X B. A FCM segmentation method of measurement of image based on adaptive coefficient of fuzzy weight[J]. Infrared Technology, 2013, 35(3):146-149. (in Chinese)

GANESH M, PALANISAMY V. Multiple-kernel fuzzy C-means algorithm for satellite image segmentation[J]. European Journal of Scientific Research, 2012, 83(2):255-263.

施海滨, 周勇.混合聚类彩色图像分割方法研究[J].计算机工程与应用, 2011, 47(9):181-185.

SHI H B, ZHOU Y. Research for color image segmentation based on hybrid clustering[J]. Computer Engineering and Applications, 2011, 47(9):181-185. (in Chinese)

汤峥, 宋余庆, 刘哲.基于粒子群优化和EM算法的图像聚类研究[J].小型微型计算机系统, 2015, 36(7):1602-1606.

TANG Z, SONG Y Q, LIU Y. Research on image clustering based on particle swarm optimization and expectation maximization algorithm[J]. Journal of Chinese Computer Systems, 2015, 36(7):1602-1606. (in Chinese)

刘璐, 吴成茂.模糊局部均值聚类分割法改进及其应用[J].计算机工程与设计, 2015, 36(12):3365-3369, 3407.

LIU L, WU C M. Improvement and application of fuzzy local mean clustering segmentation method[J]. Computer Engineering and Design, 2015, 36(12):3365-3369, 3407. (in Chinese)

HUANG H C, CHUANG Y Y, CHEN C S. Multiple kernel fuzzy clustering[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(1):120-134.

岳佳, 王士同.高斯混合模型聚类中EM算法及初始化的研究[J].微计算机信息, 2006, 22(33):244-246, 302.

YUE J, WANG S T. Algorithm EM and its initialization in Gaussian-mixture-model based clustering[J]. Control & Automation, 2006, 22(33):244-246, 302. (in Chinese)

YANG M S, LAI C Y, LIN C Y. A robust EM clustering algorithm for Gaussian mixture models[J]. Pattern Recognition, 2012, 45(11):3950-3961.

NICKABADI A, EBADZADEH M M, SAFABAKHSH R. A novel particle swarm optimization algorithm with adaptive inertia weight[J]. Applied Soft Computing, 2011, 11(4):3658-3670.

王晓辉, 吴禄慎, 陈华伟, 等.应用改进的粒子群优化模糊聚类实现点云数据的区域分割[J].光学精密工程, 2017, 25(4):1095-1105.

WANG X H, WU L S, CHEN H W, et al. Region Segmentation of point cloud data based on improved particle swarm optimization fuzzy clustering[J]. Optics and Precision Engineering, 2017, 25(4):1095-1105. (in Chinese)

RADIVOJAC P, CLARK W T, ORON T R, et al. A large-scale evaluation of Computational protein function prediction[J]. Nature Methods, 2013, 10(3):221-227.

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