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湿地遥感图像分割算法设计及实现 被引量：1

Design and realization of wetland remote sensing image segmentation method
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摘要 提出了一种结合熵和模糊C均值的聚类分割方法。模糊C均值(FCM)聚类算法广泛用于图像的自动分割,但是传统的FCM算法没有考虑像素的空间信息,因而对噪声十分敏感,基于二维直方图的模糊C均值聚类算法除了考虑像素点的灰度信息外还考虑了像素点邻域的空间信息,可有效地抑制噪声;在目标函数中引入熵项则能更好地抑制噪声和外围点对类中心估计的影响。实验分析结果表明,算法对湿地遥感图像的分割效果优于FCM算法。 A new effective multi-thresholds image segmentation method based on two-dimensional histogram FCM ＆ entropy clustering is presented.Fuzzy C-means clustering algorithm has been widely used in automated image segmentation.However,the conventional FCM algorithm is noise sensitive because of not taking account of the spatial information.Fuzzy C-means clustering algorithm based on two-dimensional histogram is robust for noise,because it utilizes the gray level information of each pixel and its spatial correlation information within the neighborhood.The entropy term is introduced in object function that can suppress noise effectively and reduce influence of estimation of the cluster centers.Experimental results indicate that the new algorithm is better than FCM algorithm.
作者 石月珍 辛动军
机构地区 长沙理工大学水利工程学院 中南林业科技大学计算机科学学院
出处 《计算机工程与应用》 CSCD 北大核心 2009年第25期160-162,共3页 Computer Engineering and Applications
基金 长沙理工大学创新团队计划No.2007CX05 博士基金No.1004154~~
关键词 模糊C均值聚类 二维直方图 熵 遥感图像 图像分割 fuzzy C-means clustering two-dimensional histogram entropy remote sensing image image segmentation
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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共引文献35

同被引文献11

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计算机工程与应用
2009年 第25期

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