@article{ref1,
title="Flood inundation area extraction method of SAR images based on deep learning",
journal="China safety science journal (CSSJ)",
year="2022",
author="Guo, W. and Yuan, H. and Xue, M. and Wei, P.",
volume="32",
number="4",
pages="177-184",
abstract="In order to improve decision⁃making ability for flood disaster emergency rescue and quickly extract flood inundation areas, an extraction method of SAR images based on deep learning was proposed. Firstly, flood inundation area extraction model of SAR images was established based on DeepLab v3 + model. Then, considering difficulty in obtaining labeled samples of SAR images, a semi⁃automatic sample making method based on optical image water index was proposed, which greatly reduced the labor and time required for annotation. Lastly, Sentinel-1 images were used for experimental analysis to verify the model's accuracy. The results show that the proposed extraction model has strong adaptability to complex surfaces. Compared with the adaptive threshold method, it features higher recognition accuracy, and better recognition effect of water edge, small area water body and thin and long linear water body in remote sensing image, with an mean Intersection over Union of 0.83. © 2022 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2022.04.026",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2022.04.026"
}