Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR025192 |
Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification | |
Goumehei, E.1; Tolpekin, V.2; Stein, A.2; Yan, W.1 | |
2019-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:8页码:7047-7059 |
文章类型 | Article |
语种 | 英语 |
国家 | Japan; Netherlands |
英文摘要 | Detection 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. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000490973700039 |
WOS关键词 | FLOOD ; MODEL ; SEGMENTATION ; DELINEATION ; SELECTION ; IMAGERY |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/185879 |
专题 | 资源环境科学 |
作者单位 | 1.Keio Univ, Grad Sch Media & Governance, Fujisawa, Kanagawa, Japan; 2.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, Dept Earth Observat Sci, Enschede, Netherlands |
推荐引用方式 GB/T 7714 | Goumehei, E.,Tolpekin, V.,Stein, A.,et al. Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification[J]. WATER RESOURCES RESEARCH,2019,55(8):7047-7059. |
APA | Goumehei, E.,Tolpekin, V.,Stein, A.,&Yan, W..(2019).Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification.WATER RESOURCES RESEARCH,55(8),7047-7059. |
MLA | Goumehei, E.,et al."Surface Water Body Detection in Polarimetric SAR Data Using Contextual Complex Wishart Classification".WATER RESOURCES RESEARCH 55.8(2019):7047-7059. |
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