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Open AccessArticle

Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas

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College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China
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Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
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School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510640, China
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Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G11XW, UK
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College of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, China
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Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089-2560, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 243; https://doi.org/10.3390/rs12020243
Received: 15 December 2019 / Revised: 7 January 2020 / Accepted: 8 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy. View Full-Text
Keywords: water inundations; heuristic and automatic water extraction (HAWE); Sentinel-1; synthetic aperture radar (SAR); Dongting Lake (China); remote sensing water inundations; heuristic and automatic water extraction (HAWE); Sentinel-1; synthetic aperture radar (SAR); Dongting Lake (China); remote sensing
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MDPI and ACS Style

Hu, S.; Qin, J.; Ren, J.; Zhao, H.; Ren, J.; Hong, H. Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas. Remote Sens. 2020, 12, 243.

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