Fuzzy Superpixels for Polarimetric SAR Images Classification

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)
214 Downloads (Pure)


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
Article number8310927
Pages (from-to)2846-2860
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Issue number5
Early online date9 Mar 2018
Publication statusPublished - Oct 2018

Bibliographical note

Funding Information:
Manuscript received January 12, 2017; revised July 30, 2017 and October 7, 2017; accepted February 23, 2018. Date of publication March 9, 2018; date of current version October 4, 2018. This work was supported in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621005, in part by the Fundamental Research Funds for the Central Universities under Grant XJS17108 and the China Postdoctoral Fund under Grant 2017M613081, in part by the National Natural Science Foundation of China under Grant U1701267, Grant 61573267, Grant 61771379, Grant 61772400, Grant 61772401, Grant 61671350, Grant 61473215, Grant 61571342, Grant 61501353, Grant 61502369, and Grant 61701361, in part by the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) under Grant B07048, in part by the Major Research Plan of the National Natural Science Foundation of China under Grant 91438201 and Grant 91438103, and in part by the Program for Cheung Kong Scholars and Innovative Research Team in University under Grant IRT_15R53. Joint Fund of the Equipment Research of Ministry of Education under Grant 6141A02022301. (Corresponding author: Yuwei Guo.) Y. Guo, L. Jiao, S. Wang, F. Liu, and W. Hua are with the Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China (e-mail:,yuweiguo18@126.com; s.wang@cs.bham.ac.uk; lchjiao@mail.xidian. edu.cn; f63liu@163.com; 492862054@qq.com).

Publisher Copyright:
© 2018 IEEE.


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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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