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Technical Note

Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study

1
Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
2
Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester St., Ottawa, ON K1S 5K2, Canada
3
Defence Research and Development Canada (DRDC), Ottawa Research Center, 3701 Carling Ave., Ottawa, ON K2K 2Y7, Canada
4
Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI 48105, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1209; https://doi.org/10.3390/rs9121209
Received: 11 October 2017 / Revised: 15 November 2017 / Accepted: 20 November 2017 / Published: 23 November 2017
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via a combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing the statistical distribution of backscatter values applied to the mean of each superpixel. Higher-order texture measures, such as variance, are used to improve accuracy by removing false positives via an additional thresholding process used to identify the boundaries of water bodies. Results applied to quad-polarized RADARSAT-2 data show that the threshold value for the variance texture measure can be approximated using a constant value for different scenes, and thus it can be used in a fully automated cleanup procedure. Compared to similar approaches, errors of omission and commission are improved with the proposed method. For example, we observed that a threshold-only approach consistently tends to underestimate the extent of water bodies compared to combined thresholding and segmentation, mainly due to the poor performance of the former at the edges of water bodies. The proposed method can be used for monitoring changes in surface water extent within wetlands or other areas, and while presented for use with radar data, it can also be used to detect surface water in optical images. View Full-Text
Keywords: water mapping; surface water; wetland; SAR; RADARSAT-2; histogram; threshold; segmentation; superpixel water mapping; surface water; wetland; SAR; RADARSAT-2; histogram; threshold; segmentation; superpixel
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MDPI and ACS Style

Behnamian, A.; Banks, S.; White, L.; Brisco, B.; Millard, K.; Pasher, J.; Chen, Z.; Duffe, J.; Bourgeau-Chavez, L.; Battaglia, M. Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study. Remote Sens. 2017, 9, 1209. https://doi.org/10.3390/rs9121209

AMA Style

Behnamian A, Banks S, White L, Brisco B, Millard K, Pasher J, Chen Z, Duffe J, Bourgeau-Chavez L, Battaglia M. Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study. Remote Sensing. 2017; 9(12):1209. https://doi.org/10.3390/rs9121209

Chicago/Turabian Style

Behnamian, Amir, Sarah Banks, Lori White, Brian Brisco, Koreen Millard, Jon Pasher, Zhaohua Chen, Jason Duffe, Laura Bourgeau-Chavez, and Michael Battaglia. 2017. "Semi-Automated Surface Water Detection with Synthetic Aperture Radar Data: A Wetland Case Study" Remote Sensing 9, no. 12: 1209. https://doi.org/10.3390/rs9121209

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