基于可变类FCM算法的多光谱遥感影像分割

doi:10.11999/JEIT170397

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Abstract In order to automatically determine the number of clusters in multispectral remote sensing image segmentation, Fuzzy C-Means (FCM) algorithm with unknown number of clusters is proposed. First of all, a new dissimilarity measure between a pixel and a cluster is defined. The fuzzy membership function and cluster center are obtained through minimizing the objective function. Then, the relationship between fuzzy factor and the number of clusters is studied. The optimal fuzzy factor is selected by defining the Partition Entropy (PE) index and corresponding to the minimum of fuzzy factor after the convergence of PE values. According to the relationship between the fuzzy factor and the number of clusters, the optimal number of clusters is obtained, and the variable cluster segmentation of the image is realized. The analysis based on segmentation results of synthesized image and real multispectral remote sensing images show that the proposed algorithm can automatically determine the number of clusters and obtain the ideal segmentation results simultaneously. It provides a new method for automatically determine the number of clusters of remote sensing image.

Key words： Multispectral remote sensing image Fuzzy membership degree Fuzzy factor Segmentation with unknown number of clusters Partition Entropy (PE) index

Received: 02 May 2017 Published: 01 November 2017

PACS:

TP751

Corresponding Authors: LIU Xiaoyan E-mail: ltalexy@163.com

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	ZHAO Quanhua
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	LI Yu

Cite this article:

ZHAO Quanhua,LIU Xiaoyan,ZHAO Xuemei等. Multispectral Remote Sensing Image Segmentation Based on FCM Algorithm with Unknown Number of Clusters[J]. JEIT, 2018, 40(1): 157-165.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170397 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I1/157

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