A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification

Hua Zhang, Qunming Wang, Wenzhong Shi, Ming Hao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)


This paper presents a novel adaptive fuzzy local information c-means (ADFLICM) clustering approach for remotely sensed imagery classification by incorporating the local spatial and gray level information constraints. The ADFLICM approach can enhance the conventional fuzzy c-means algorithm by producing homogeneous segmentation and reducing the edge blurring artifact simultaneously. The major contribution of ADFLICM is use of the new fuzzy local similarity measure based on pixel spatial attraction model, which adaptively determines the weighting factors for neighboring pixel effects without any experimentally set parameters. The weighting factor for each neighborhood is fully adaptive to the image content, and the balance between insensitiveness to noise and reduction of edge blurring artifact to preserve image details is automatically achieved by using the new fuzzy local similarity measure. Four different types of images were used in the experiments to examine the performance of ADFLICM. The experimental results indicate that ADFLICM produces greater accuracy than the other four methods and hence provides an effective clustering algorithm for classification of remotely sensed imagery.

Original languageEnglish
Article number7934118
Pages (from-to)5057-5068
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number9
Publication statusPublished - Sep 2017


  • Classification
  • fuzzy c-means (FCM) clustering
  • local measure similarity
  • remotely sensed imagery
  • spatial information

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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