This paper presents a two-phase fuzzy clustering algorithm based on neurodynamic optimization with its application for polarimetric synthetic aperture radar (PolSAR) remote sensing image segmentation. The two-phase clustering algorithm starts with the linear-assignment initialization phase with the least similar cluster representatives to remedy the inconsistency of clustering results from random initialization and is, then, followed with multiple-kernel fuzzy C-means clustering. By incorporating multiple kernels in the clustering framework, various features are incorporated cohesively. A winner-takes-all neural network is employed to acquire the highest kernel weights and associated cluster centers and membership matrices, which enables better characterization and adaptability in each individual cluster. Simulation results for UCI benchmark datasets and PolSAR remote sensing image segmentation are reported to substantiate the effectiveness and the superiority of the proposed clustering algorithm.

中文翻译：

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一种基于神经动力学优化的两阶段模糊聚类算法及其在PolSAR图像分割中的应用

本文提出了一种基于神经动力学优化的两阶段模糊聚类算法，并将其应用于极化合成孔径雷达 (PolSAR) 遥感图像分割。两阶段聚类算法从线性分配初始化阶段开始，用最少相似的聚类代表来弥补随机初始化聚类结果的不一致，然后是多核模糊 C 均值聚类。通过在聚类框架中合并多个内核，各种功能被紧密地结合在一起。赢家通吃的神经网络被用来获取最高的核权重和相关的集群中心和成员矩阵，这使得每个集群的特征和适应性更好。