Abstract
AbstractDetection 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.
Highlights
Surface water body detection using satellite data has been addressed in many studies
Results were compared with the Wishart Maximum Likelihood Classification (WMLC), the Gaussian Maximum Likelihood Classification, and a median filter followed by thresholding
The Digital Elevation Model for terrain correction was the Shuttle Radar Topography Mission (SRTM) elevation model of a 3‐arcsecond resolution, whereas the nearest neighbor method was chosen as the resampling method
Summary
Surface water body detection using satellite data has been addressed in many studies. There is at present insufficient knowledge of the spatial and temporal dynamics of available surface water (Alsdorf et al, 2007), since the variation of the spatial extent of inland water bodies both seasonally and interannually is strong (Papa et al, 2010). Over 70% of global permanent water loss has occurred in five countries Iran is among these five countries with 56% loss of permanent surface water between 1984 and 2015 (Pekel et al, 2016). Such losses raise major concerns of water security and sustainability. It is important to obtain accurate and updated information about the distribution of available surface water bodies
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