丁玉琦, 邵振峰, 胡石元. 一种云模型和期望最大聚类的遥感影像分割算法[J]. 武汉大学学报 ( 信息科学版), 2015, 40(6): 721-726. DOI: 10.13203/j.whugis20130483
引用本文: 丁玉琦, 邵振峰, 胡石元. 一种云模型和期望最大聚类的遥感影像分割算法[J]. 武汉大学学报 ( 信息科学版), 2015, 40(6): 721-726. DOI: 10.13203/j.whugis20130483
DING Yuqi, SHAO Zhenfeng, HU Shiyuan. Combined with Cloud Model and EM Clustering for Remote Sensing Image Segmentation[J]. Geomatics and Information Science of Wuhan University, 2015, 40(6): 721-726. DOI: 10.13203/j.whugis20130483
Citation: DING Yuqi, SHAO Zhenfeng, HU Shiyuan. Combined with Cloud Model and EM Clustering for Remote Sensing Image Segmentation[J]. Geomatics and Information Science of Wuhan University, 2015, 40(6): 721-726. DOI: 10.13203/j.whugis20130483

一种云模型和期望最大聚类的遥感影像分割算法

Combined with Cloud Model and EM Clustering for Remote Sensing Image Segmentation

  • 摘要: 为解决遥感影像分割中存在的不确定性问题和传统层次聚类算法中存在的时间复杂度高、缺乏可再分性等缺陷,基于云模型和期望最大聚类提出了一种新的遥感影像分割算法。该算法首先使用峰值法云变换从影像中抽取底层概念,然后通过EM算法对底层概念进行聚类,最后通过极大判别法完成遥感影像分割。实验证明,EM算法进行概念聚类能够快速地将概念分类为指定个数,并估计出高阶云概念的数学特征,相比于传统的基于云模型的遥感影像分割算法具有更好的分割效果。

     

    Abstract: To solve the shortcomings of the traditional hierarchical clustering algorithm for remotesensing image segmentation,in this paper we propose a new algorithm based on a cloud model and EMalgorithm.In the proposed algorithm,we extract the bottom concepts from the remote sensing imageusing apeak method for cloud transform,and get higher cloud concepts through the EM algorithm,and finally segment the image with Great Criterion.The results show that clustering concepts can beclassified quickly as a specified number by the EM algorithm for estimatation of the mathematical fea-tures of the higher clouds.The proposed algorithm is superior to the traditional methods.

     

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