Next Article in Journal
Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic
Next Article in Special Issue
Time Series Analysis of Very Slow Landslides in the Three Gorges Region through Small Baseline SAR Offset Tracking
Previous Article in Journal
Online Global Land Surface Temperature Estimation from Landsat
Previous Article in Special Issue
Identification of C-Band Radio Frequency Interferences from Sentinel-1 Data
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessTechnical Note
Remote Sens. 2017, 9(12), 1209;

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

Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 560 Rochester St., Ottawa, ON K1S 5K2, Canada
Defence Research and Development Canada (DRDC), Ottawa Research Center, 3701 Carling Ave., Ottawa, ON K2K 2Y7, Canada
Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI 48105, USA
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [6229 KB, uploaded 14 March 2018]   |  


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

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top