Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification

Elham Goumehei, V.A. Tolpekin*, A. Stein, Wanglin Yan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)
324 Downloads (Pure)


Detection of surface water from satellite images is important for water management purposes like for mapping flood extents, inundation dynamics, and water resources distributions. In this research, we introduce a supervised contextual classification model to detect surface water bodies from polarimetric Synthetic Aperture Radar (SAR) data. A complex Wishart Markov Random Field (WMRF) combines Markov Random Fields with the complex Wishart distribution. It is applied on Single Look Complex Sentinel 1 data. Using Markov Random Fields, we utilize the geometry of surface water to remove speckle from SAR images. Results were compared with the Wishart Maximum Likelihood Classification (WMLC), the Gaussian Maximum Likelihood Classification, and a median filter followed by thresholding. Experiments demonstrate that the statistical representation of data using the Wishart distribution improves the F‐score to 0.95 for WMRF, while it is 0.67, 0.88, and 0.91 for Gaussian Maximum Likelihood Classification, WMLC, and thresholding, respectively. The main improvement in the precision increases from 0.80 and 0.86 for WMLC and thresholding to 0.96 for WMRF. The WMRF model accurately distinguishes classes that have a similar backscatter, like water and bare soil. Hence, the high accuracy of the proposed WMRF model is a result of its robustness for water detection from Single Look Complex data. We conclude that the proposed model is a great improvement on existing methods for the detection of calm surface water bodies.
Original languageEnglish
Article number025192
Pages (from-to)7047-7059
Number of pages13
JournalWater resources research
Issue number8
Early online date30 Jul 2019
Publication statusPublished - 1 Aug 2019


  • UT-Hybrid-D
  • supervised contextual classification model
  • surface water detection
  • Single Look Complex (SLC) SAR images
  • complex Wishart Markov Random Fields (WMRF)


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