Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning
Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning
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DOI：
10.1029/2021gl096007
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发表时间：
2022-01
影响因子：
5.2
通讯作者：
Shuangshang Zhang-;Qinghong Xu;Haoyu Wang;Yanyan Kang;Xiaofeng Li
中科院分区：
文献类型：
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作者：
Shuangshang Zhang-;Qinghong Xu;Haoyu Wang;Yanyan Kang;Xiaofeng Li
本研究提出了一种直观的方法来推导大型滩涂的数字高程模型（DEM）。我们首先开发了一种基于深度卷积神经网络的自动化方法，从 2015 年至 2020 年在中国黄海沿岸苏北沙洲采集的合成孔径雷达图像中准确提取水线。统计结果表明，即使在复杂的成像条件下，该方法也能有效地提取水线，其平均召回率和精度分别为 0.90 和 0.80。然后利用全球潮汐模型对像素级提取的水线进行校准，构建研究区大尺度滩涂的DEM。与现场地形数据的比较显示误差为29 cm，证明了监测潮间带地貌沉积演化的有用性。此外，2015年至2020年，苏北沙洲保持稳定，而沿海地区则因人类活动而发生了巨大变化。
This study presented an intuitive approach to derive large‐scale tidal flat's Digital Elevation Model (DEM). We first developed an automated method for accurately extracting the waterline from Synthetic Aperture Radar images acquired in Subei Sandbanks along the Yellow Sea coast of China between 2015 and 2020 based on deep convolutional neural networks. The statistical results show this method has appreciable accuracy for efficient waterline extraction even under complex imaging conditions with a mean recall and precision of 0.90 and 0.80, respectively. Then the pixel‐level extracted waterlines are calibrated with a global tide model to construct the large‐scale tidal flat's DEM in the study region. The comparison against in situ topographic data shows an error of 29 cm, demonstrating the usefulness of monitoring the morpho‐sedimentary evolution in intertidal areas. Furthermore, the Subei Sandbanks remained stable from 2015 to 2020, while the coastal region changed drastically due to human activities.