基于混合邻域约束项的改进FCM算法
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辽宁工程技术大学

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中图分类号:

TP391

基金项目:

辽宁省教育厅科学技术研究资助项目(No.LJ2019JL001)


Mixed neighborhood constraints based Fuzzy C-Means
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Affiliation:

Liaoning Technical University

Fund Project:

The Project of Science and Technology Research of Education Department of Liaoning Province(NO.LJ2019JL001)

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    摘要:

    传统模糊聚类算法在影像分割过程中,仅考虑影像的光谱信息,所以对噪声比较敏感。为了克服传统模糊聚类分割算法对噪声的敏感性,提出基于混合邻域约束项的改进模糊C均值聚类(Fuzzy C-Means Clustering, FCM)算法。该算法首先从隶属性及光谱属性两方面定义邻域像素关于中心像素的相似度,然后利用线性加权的方式将两种相似度进行融合,同时结合邻域像素到聚类中心的距离构造邻域约束项,并将其引入目标函数中,以平衡影像分割过程中的影像平滑及细节保留,实现更优分割。通过对合成影像及真实遥感影像分割结果的定性、定量评价,验证了该算法具有较强的鲁棒性,在降低了对噪声的敏感性的同时能够较好的保留影像细节,获得高精度的分割结果。

    Abstract:

    Traditional fuzzy clustering based segmentation algorithms are sensitive to noise. In order to enhance the robustness, an improved FCM algorithm based on neighborhood similarity is proposed. Firstly, similarities between the center pixel and its neighbour pixels are defined from the spectral and membership characteristics, respectively. Secondly, the neighbor constraint item is defined combining the two similarities and the distance from each neighborhood pixel to the cluster centers. Then the objective function of the proposed Mixed neighborhood constraints based Fuzzy C-Means (MNCFCM) algorithm is defined by adding the neighbor constraint item in order to keep balance between image smoothing and details preserving during segmentation.Through qualitative and quantitative evaluation of the segmentation results of the composite image and the real remote sensing image, the algorithm is robust to noise and can preserve image details at the same time,which obtains highly accurate segmentation results .

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历史
  • 收稿日期:2019-09-19
  • 最后修改日期:2020-12-21
  • 录用日期:2020-03-17
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