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Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification

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

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Authors

Yuwei Guo

Licheng Jiao

Profile Image

RONG QU rong.qu@nottingham.ac.uk
Professor of Computer Science

Zhuangzhuang Sun

Shuang Wang

Shuo Wang

Fang Liu



Abstract

The increasing applications of polarimetric synthetic aperture radar (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. First, 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. Second, 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 superpixel (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.

Journal Article Type Article
Acceptance Date Oct 19, 2021
Online Publication Date Nov 23, 2021
Publication Date 2022
Deposit Date Feb 20, 2022
Publicly Available Date Mar 1, 2022
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 60
Article Number 5217818
DOI https://doi.org/10.1109/TGRS.2021.3128908
Keywords General Earth and Planetary Sciences; Electrical and Electronic Engineering
Public URL https://nottingham-repository.worktribe.com/output/7477746
Publisher URL https://ieeexplore.ieee.org/document/9625939
Additional Information © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted
component of this work in other works.

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