Open AccessArticle
Water Detection in Urban Areas from GF-3
by
Xiaoyan Liu 1,2,3,4, Long Liu 1,2,3,*, Yun Shao 1,2,3, Quanhua Zhao 4, Qingjun Zhang 5 and Linjiang Lou 6
1
Laboratory of Target Microwave Properties, Deqing 313200, China
2
Deqing Academy of Satellite Applications, Deqing 313200, China
3
Laboratory of Radar Remote Sensing Application Technology, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
4
Institute of Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin 123000, China
5
China Academy of Space Technology, Beijing Institute of Space System Engineering, Beijing 100086, China
6
Satellite Surveying and Mapping Application Center, Beijing 100048, China
Cited by 7 | Viewed by 4468
Abstract
The rapid and accurate detection of urban water is critical for urban management, river detection, and flood disaster assessment. This study is devoted to detecting water by GaoFen-3 (GF-3) Synthetic Aperture Radar (SAR) images with high spatial resolution. There have been no effective
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The rapid and accurate detection of urban water is critical for urban management, river detection, and flood disaster assessment. This study is devoted to detecting water by GaoFen-3 (GF-3) Synthetic Aperture Radar (SAR) images with high spatial resolution. There have been no effective solutions that discriminate water and building shadows using a single SAR image in previous research. Inspired by the principle that every shadow has a corresponding building nearby, a new method is proposed in this study, whereby building shadows are removed depending on the correspondence of buildings and their shadows. The proposed method is demonstrated effective and efficient by experimental results on six GF-3 SAR images. The Receiver Operating Characteristic (ROC) curves of the water detection results indicate that the proposed method increases the Probability of Detection (PD) to 98.36% and decreases the Probability of False Alarm (PFA) to 1.91% compared with the thresholding method, where, at the same PFA level, the maximum PD of the thresholding method is 72.62% in all testing samples. The proposed method is capable of removing building shadows and detecting water with high precision in urban areas, which presents the great potential of high-spatial-resolution GF-3 images in terms of water resource management.
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