Fuzzy-topology-integrated support vector machine for remotely sensed image classification

Hua Zhang, Wen Zhong Shi, Kimfung Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


This paper presents a novel fuzzy-topologyintegrated support vector machine (SVM) (FTSVM) classification method for remotely sensed images based on the standard SVM. Induced threshold fuzzy topology is integrated into the standard SVM. First, the optimal intercorrelation coefficient threshold value is applied to decompose an image class in spectral space into the three parts: interior, boundary, and exterior in fuzzy-topology space. The interior-class pixels are then classified as predefined classes based on maximum likelihood. The exterior-class pixels are ignored. The fuzzy-boundary-class pixels which contain misclassified pixels are reclassified based on the fuzzy-topology connectivity theory. As a result, misclassified pixel problems, to a certain extent, are solved. Two different experiments were performed to evaluate the performance of the FTSVM method, in comparison with standard SVM, maximum likelihood classifier (MLC), and fuzzy-topology-integrated MLC. Experimental results indicate that the FTSVM method performs better than the standard SVM and other methods in terms of classification accuracy, hence providing an effective classification method for remotely sensed images.
Original languageEnglish
Article number6029294
Pages (from-to)850-862
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number3
Publication statusPublished - 1 Mar 2012


  • Classification
  • Fuzzy topology
  • Remote sensing
  • Support vector machine (SVM)
  • Threshold

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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