A Two-Phase Fuzzy Clustering Algorithm Based on Neurodynamic Optimization with Its Application for PolSAR Image Segmentation

Jianchao Fan, Jun Wang

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number7778198
Pages (from-to)72-83
Number of pages12
JournalIEEE Transactions on Fuzzy Systems
Volume26
Issue number1
DOIs
Publication statusPublished - Feb 2018
Externally publishedYes

Keywords

  • Fuzzy C-means (FCM) clustering
  • linear-assignment initialization
  • multiple kernels
  • polarimetric synthetic aperture radar (PolSAR) image segmentation
  • winner-takes-all (WTA) neural network

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