Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification

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

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.

Citation

Guo, Y., Jiao, L., Qu, R., Sun, Z., Wang, S., Wang, S., & Liu, F. (2022). Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5217818. https://doi.org/10.1109/TGRS.2021.3128908

• 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
• 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.