基于混合邻域约束项的改进FCM算法

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.对此,提出基于混合邻域约束项的改进模糊C均值聚类(MNCFCM)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从两方面定义的相似度进行融合,同时结合邻域像素到聚类中心的欧氏距离构造混合邻域约束项,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,实现对影像的更优分割;最后,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,验证所提出算法具有较强的鲁棒性,在降低对噪声的敏感性的同时,能够较好地保留影像细节,获得高精度的分割结果.

Mixed neighborhood constraints based fuzzy C-means algorithm

Traditional fuzzy clustering based segmentation algorithms are sensitive to noise. Therefore, an improved fuzzy C-means clustering(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. Finally, through qualitative and quantitative evaluation of the segmentation results of the composite image and the real remote sensing image, it is verified that the algorithm is robust to noise and can preserve image details at the same time, which can obtain highly accurate segmentation results.