A fuzzy topology-based maximum likelihood classification

Kimfung Liu, Wen Zhong Shi, Hua Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)


Classification is one of the most widely used remote sensing analysis techniques, with the maximum likelihood classification (MLC) method being a major tool for classifying pixels from an image. Fuzzy topology, in which the set concept is generalized from two values, {0, 1}, to the values of a continuous interval, [0, 1], is a generalization of ordinary topology and is used to solve many GIS problems, such as spatial information management and analysis. Fuzzy topology is induced by traditional thresholding and as such gives a decomposition of MLC classes.Presented in this paper is an image classification modification, by which induced threshold fuzzy topology is integrated into the MLC method (FTMLC). Hence, by using the induced threshold fuzzy topology, each image class in spectral space can be decomposed into three parts: an interior, a boundary and an exterior. The connection theory in induced fuzzy topology enables the boundary to be combined with the interior. That is, a new classification method is derived by integrating the induced fuzzy topology and the MLC method. As a result, fuzzy boundary pixels, which contain many misclassified and over-classified pixels, are able to be re-classified, providing improved classification accuracy. This classification is a significantly improved pixel classification method, and hence provides improved classification accuracy.
Original languageEnglish
Pages (from-to)103-114
Number of pages12
JournalISPRS Journal of Photogrammetry and Remote Sensing
Issue number1
Publication statusPublished - 1 Jan 2011


  • Fuzzy topology
  • Land cover mapping
  • Maximum likelihood classification (MLC)
  • Remote sensing
  • Thresholding

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences


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