Deep-Learning-Based Marine Aquaculture Zone Extractions From Dual-Polarimetric SAR Imagery
文献类型：期刊论文
| 作者 | Chen, Wantai1,2; Li, Xiaofeng1,3 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 2024 |
| 卷号 | 17页码:8043-8057 |
| 关键词 | Aquaculture Radar polarimetry Imaging Tides Monitoring Task analysis Radar imaging Deep convolution neural networks (DCNNs) imaging characteristics marine aquaculture zones (MAZs) Sentinel-1 synthetic aperture radar (SAR) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2024.3384511 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | Efficient monitoring of marine aquaculture zones (MAZs) is crucial for facilitating coastal resource management. To achieve this, we developed a specialized deep convolutional neural network tailored for extracting MAZs from synthetic aperture radar (SAR) imagery, integrating prior analytical knowledge of MAZ imaging features. A total of 47 Sentinel-1 dual-polarized (VV and VH) SAR images spanning 2016-2023 in China's Subei Sandbanks along the Yellow Sea coast were collected due to appropriate tidal level and acquired time. We first comprehensively analyzed of normalized radar cross section (NRCS) values for MAZs under varying tidal levels and aquaculture facility structures. Rising tide-induced submergence resulted in a significant mean NRCS reduction of 7.01 dB (VV) and 4.54 dB (VH), causing MAZ signals to resemble seawater. In addition, during low tide, volume scattering from the net screen on the aquaculture rafts increased VH-polarized image recognizability, with a smaller NRCS overlap (64%) between MAZs and tidal flats compared to VV-polarized images. Hence, VH-polarized images taken during low tide with intact aquaculture facilities were selected for dataset construction due to their reliability in characterizing MAZs. Building upon the classical U-Net framework, we introduced four modifications informed by our imaging characteristics analysis to enhance the model's performance. Testing experiments demonstrated an impressive F1-score of 94.77%, highlighting the effectiveness of incorporating prior knowledge into refining deep learning models. Applying the model to SAR images from 2016 to 2023 revealed concentrated MAZs in the relatively flat southeastern Subei Sandbanks, with a noticeable scale decline post-2021 resulting in a 67.65% reduction over the years. |
| WOS关键词 | TIDAL FLATS ; GREEN ; AREA |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001205792300012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/185598]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Coll Marine Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Wantai,Li, Xiaofeng. Deep-Learning-Based Marine Aquaculture Zone Extractions From Dual-Polarimetric SAR Imagery[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:8043-8057. |
| APA | Chen, Wantai,&Li, Xiaofeng.(2024).Deep-Learning-Based Marine Aquaculture Zone Extractions From Dual-Polarimetric SAR Imagery.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,8043-8057. |
| MLA | Chen, Wantai,et al."Deep-Learning-Based Marine Aquaculture Zone Extractions From Dual-Polarimetric SAR Imagery".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):8043-8057. |
入库方式： OAI收割
来源：海洋研究所
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