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dc.contributor.authorBi, Haixia
dc.contributor.authorXu, Lin
dc.contributor.authorCao, Xiangyong
dc.contributor.authorXue, Yong
dc.contributor.authorXu, Zongben
dc.date.accessioned2020-06-05T15:52:13Z
dc.date.available2020-06-05T15:52:13Z
dc.date.issued2020-06-02
dc.identifier.citationYong, X. (2020). 'Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field'. IEEE Transactions on Image Processing, pp. 1-14.en_US
dc.identifier.issn1057-7149
dc.identifier.doi10.1109/TIP.2020.2992177
dc.identifier.urihttp://hdl.handle.net/10545/624876
dc.description.abstractPolarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper.With a newly defined channelwise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=83en_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/9106810en_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectData Science, Image Processing, AIen_US
dc.titlePolarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random fielden_US
dc.typeArticleen_US
dc.identifier.eissn1941-0042
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity of Bristolen_US
dc.contributor.departmentShanghai Em-Data Technology Co., Ltd.en_US
dc.contributor.departmentXi’an Jiaotong University, Xi’an, Chinaen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalIEEE Transactions on Image Processingen_US
dcterms.dateAccepted2020-05
refterms.dateFOA2020-06-05T15:52:14Z
dc.author.detail785299en_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International