Image change detection based on an improved rough fuzzy c-means clustering algorithm

被引：0
|
作者：
Wenping Ma
Licheng Jiao
Maoguo Gong
Congling Li
机构：
[1] Xidian University,Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China
来源：
International Journal of Machine Learning and Cybernetics | 2014年 / 5卷
关键词：
Change detection; Rough set; Fuzzy cluster algorithm; Remote sensing image;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
An unsupervised change detection method based on an improved rough fuzzy c-means clustering method (SRFPCM) for synthetic aperture radar and optical remote sensing images is proposed. SRFPCM incorporates the local spatial information and gray level information in a novel fuzzy way, aiming at guaranteeing noise insensitiveness and image detail preservation. Inspired by the idea of a robust fuzzy local information c-means clustering algorithm, this new algorithm can overcome the disadvantages of rough fuzzy c-means clustering algorithm and enhance the clustering performance at the same time. SRFPCM is employed to cluster the difference image into two clusters (changed and unchanged regions) and get the change map. Experimental results confirm the effectiveness of the proposed algorithm.
引用
收藏
页码：369 / 377
页数：8
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