Adaptive fuzzy learning superpixels representation for PolSAR image classification

Yuwei Guo, Licheng Jiao, Rong Qu, Zhuangzhuang Sun, Shuang Wang, Shuo Wang, Fang Liu

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The increasing applications of Polarimetric SAR (PolSAR) image classification demand for effective superpixels algorithms. Fuzzy superpixels algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance, and undetermined pixels. However, two key issues remain to be addressed in designing a fuzzy superpixel algorithm for PolSAR image classification. Firstly, the polarimetric scattering information, which is unique in PolSAR images, is not effectively used. Such information can be utilized to generate superpixels more suitable for PolSAR images. Secondly, the ratio of undetermined pixels is fixed for each image in the existing techniques, ignoring the fact that the difficulty of classifying different objects varies in an image. To address these two issues, we propose a polarimetric scattering information based adaptive fuzzy superpixels (AFS) algorithm for PolSAR images classification. In AFS, the correlation between pixels’ polarimetric scattering information, for the first time, is considered through fuzzy rough set theory to generate superpixels. This correlation is further used to dynamically and adaptively update the ratio of undetermined pixels. AFS is evaluated extensively against different evaluation metrics and compared with the state-of-the-art superpixels algorithms on three PolSAR images. The experimental results demonstrate the superiority of AFS on PolSAR image classification problems.
Original languageEnglish
Article number9625939
JournalIEEE Transactions on Geoscience and Remote Sensing
Publication statusPublished - 23 Nov 2021


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


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