Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field
- Submitting institution
-
University of Derby
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 785299-2
- Type
- D - Journal article
- DOI
-
10.1109/TIP.2020.2992177
- Title of journal
- IEEE Transactions on Image Processing
- Article number
- -
- First page
- 6601
- Volume
- 29
- Issue
- -
- ISSN
- 1057-7149
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
-
https://ieeexplore.ieee.org/document/9106810
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. We presented a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise 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. 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, ensuring accurate and smooth segmentation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
