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SAR image water extraction using the attention U-net and multi-scale level set method:flood monitoring in South China in 2020 as a test case

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Geo-Spatial Information Science 2022年第2期25卷 155-168页

作者： Chuan Xu Shanshan Zhang Bofei Zhao Chang Liu Haigang Sui Wei Yang Liye Mei School of Computer Science Hubei University of TechnologyWuhanChina Geographic Information Center GuangZhou Urban Planning&Design Survey Research InstituteGuangzhouChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Information Science and Engineering Wuchang Shouyi UniversityWuhanChina The Institute of Technological Sciences Wuhan UniversityWuhanChina

Level set method has been extensively used for image segmentation,which is a key technology of water ***,one of the problems of the level-set method is how to find the appropriate initial surface parameters,which will affect the accuracy and speed of level set ***,the semantic segmentation based on deep learning has opened the exciting research *** addition,the Convolutional Neural Network(CNN)has shown a strong feature representation ***,in this paper,the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve,which only needs to describe the general outline of the water body,rather than the accurate *** with the traditional circular and rectangular zero-level set initialization method,this method can converge to the edge of the water body faster and more precisely;it will not fall into the local minimum value and be able to obtain accurate segmentation *** effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.

关键词： Water extraction flood monitoring level set attention U-net Convolutional Neural Network(CNN)

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Evaluation of GSMaP Daily Rainfall Satellite Data for flood monitoring: Case Study—Kyushu Japan

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Journal of Geoscience and Environment Protection 2016年第12期4卷 101-117页

作者： Martiwi Diah Setiawati Fusanori Miura Integrated Research System for Sustainability Science (IR3S) The University of Tokyo Bunkyo Japan Graduate School of Science and Engineering Yamaguchi University Ube Japan

In this paper, the Global Satellite Mapping of Precipitation Moving Vector with Kalman filter (GSMaP_MVK) was evaluated and corrected at daily time scales with a spatial resolution of 0.1°;latitude/longitude. The reference data came from thirty-four rain gauges on Kyushu Island, Japan. This study focused on the GSMaP_MVK’s ability to detect heavy rainfall patterns that may lead to flooding. Statistical analysis was used to evaluate the GSMaP_MVK data both quantitatively and qualitatively. The statistical analysis included the relative bias (B), the mean error (E), the Nash-Sutcliffe coefficient (CNS), the Root Mean Square Error (RMSE) and the correlation coefficient (r). In addition, Generalized Additive Models (GAMs) were used to conduct GSMaP_MVK data correction. The results of these analyses indicate that GSMaP_MVK data have lower values than observed data and may be significantly underestimated during heavy rainfall. By applying GAM to bias correction, GSMaP_MVK’s ability to detect heavy rainfall was improved. In addition, GAM for bias correction could effectively be applied for significant underestimates of GSMaP_ MVK (i.e., bias of more than 55%). GAM is a new approach to predict rainfall amount for flood and landslide monitoring of satellite base precipitation, especially in areas where rain gauge data are limited.

关键词： Evaluation GSMaP_MVK flood monitoring Kyushu

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