Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas
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 thresholdin...
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doaj-6865a88ba6de41f8ad38ccd3207c5a282020-11-25T01:27:50ZengMDPI AGRemote Sensing2072-42922020-01-0112224310.3390/rs12020243rs12020243Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain AreasShunshi Hu0Jianxin Qin1Jinchang Ren2Huimin Zhao3Jie Ren4Haoran Hong5College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, ChinaCollege of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, ChinaSchool of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510640, ChinaSchool of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510640, ChinaCollege of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, ChinaMing Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089-2560, USAAccurately 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.https://www.mdpi.com/2072-4292/12/2/243water inundationsheuristic and automatic water extraction (hawe)sentinel-1synthetic aperture radar (sar)dongting lake (china)remote sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shunshi Hu Jianxin Qin Jinchang Ren Huimin Zhao Jie Ren Haoran Hong |
spellingShingle |
Shunshi Hu Jianxin Qin Jinchang Ren Huimin Zhao Jie Ren Haoran Hong Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas Remote Sensing water inundations heuristic and automatic water extraction (hawe) sentinel-1 synthetic aperture radar (sar) dongting lake (china) remote sensing |
author_facet |
Shunshi Hu Jianxin Qin Jinchang Ren Huimin Zhao Jie Ren Haoran Hong |
author_sort |
Shunshi Hu |
title |
Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas |
title_short |
Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas |
title_full |
Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas |
title_fullStr |
Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas |
title_full_unstemmed |
Automatic Extraction of Water Inundation Areas Using Sentinel-1 Data for Large Plain Areas |
title_sort |
automatic extraction of water inundation areas using sentinel-1 data for large plain areas |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-01-01 |
description |
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. |
topic |
water inundations heuristic and automatic water extraction (hawe) sentinel-1 synthetic aperture radar (sar) dongting lake (china) remote sensing |
url |
https://www.mdpi.com/2072-4292/12/2/243 |
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