Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field
Journal article
Authors | Bi, Haixia, Xu, Lin, Cao, Xiangyong, Xue, Yong and Xu, Zongben |
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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. |
Keywords | Data Science, Image Processing, AI |
Year | 2020 |
Journal | IEEE Transactions on Image Processing |
Publisher | IEEE |
ISSN | 1057-7149 |
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 dates | 02 Jun 2020 |
Publication process dates | |
Deposited | 05 Jun 2020, 15:52 |
Accepted | May 2020 |
Rights | Attribution 4.0 International |
Contributors | University of Derby, University of Bristol, Shanghai Em-Data Technology Co., Ltd., Xi’an Jiaotong University, Xi’an, China and University of Derby |
File | File Access Level Open |
File | File Access Level Open |
File | File Access Level Open |
https://repository.derby.ac.uk/item/93z0z/polarimetric-sar-image-semantic-segmentation-with-3d-discrete-wavelet-transform-and-markov-random-field
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