Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field

Journal article


Bi, Haixia, Xu, Lin, Cao, Xiangyong, Xue, Yong and Xu, Zongben 2020. Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2020.2992177
AuthorsBi, Haixia, Xu, Lin, Cao, Xiangyong, Xue, Yong and Xu, Zongben
Abstract

Polarimetric 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.

KeywordsData Science, Image Processing, AI
Year2020
JournalIEEE Transactions on Image Processing
PublisherIEEE
ISSN1057-7149
1941-0042
Digital Object Identifier (DOI)https://doi.org/10.1109/TIP.2020.2992177
Web address (URL)http://hdl.handle.net/10545/624876
http://creativecommons.org/licenses/by/4.0/
hdl:10545/624876
Publication dates02 Jun 2020
Publication process dates
Deposited05 Jun 2020, 15:52
AcceptedMay 2020
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Attribution 4.0 International

ContributorsUniversity of Derby, University of Bristol, Shanghai Em-Data Technology Co., Ltd., Xi’an Jiaotong University, Xi’an, China and University of Derby
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