Automatic Waterline Extraction of Tidal Flats from SAR Images Based on Deep Convolutional Neural Networks

被引：0
|
作者：
Zhang, Shuangshang [1 ,2 ]
Xu, Qing [3 ]
Li, Xiaofeng [1 ,2 ]
机构：
[1] Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China
[2] Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Peoples R China
[3] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China
来源：
2022 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM (PIERS 2022) | 2022年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/PIERS55526.2022.9792855
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this study, we proposed an automatic waterline signature extraction method based on deep convolutional neural networks (DCNNs). Our objective is to provide a rapid and straightforward to use method that can tackle the waterline signature extraction from large-scale tidal flats in Sentinel-1 SAR images without re-training or manual interference. The statistical results show this DCNN-based method has appreciable accuracy for efficient extraction of waterline in SAR images even under complex imaging conditions (the mean precision and recall are 0.81 and 0.88, respectively), implying that this method is potential for rapid analysis of tidal flat topography evolution by using the waterline method.
引用
收藏
页码：273 / 277
页数：5
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