Fuzzy superpixels for polarimetric SAR images classification

Yuwei Guo, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu, Wenqiang Hua

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
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Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image. Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy superpixels, is proposed for reducing the generation of mixed superpixels. In fuzzy superpixels, not all pixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assigning them to improper superpixels. Second, a new algorithm, named FuzzyS (FS), is proposed to generate fuzzy superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed FS algorithm. Experimental results demonstrate the superiority of the proposed FS algorithm over several state-of-the-art superpixels algorithms.
Original languageEnglish
Pages (from-to)2846-2860
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Issue number5
Early online date9 Mar 2018
Publication statusPublished - 4 Oct 2018


  • fuzzy superpixels
  • image classification
  • polarimetric synthetic aperture radar (PolSAR)
  • superpixels


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