IW Extraction From SAR Images Based on Generative Networks With Small Datasets
文献类型：期刊论文
| 作者 | Saheya, B.2,3; Ren, Xia3; Gong, Maoguo2,3; Li, Xiaofeng1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:16473-16487 |
| 关键词 | Feature extraction Training Generative adversarial networks Radar polarimetry Data models Oceans Accuracy Synthetic aperture radar Generators Biological system modeling Downsampling generative adversarial network (GAN) internal wave (IW) synthetic aperture radar (SAR) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3576391 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | Internal waves (IWs), a significant phenomenon in various marine environments, play a crucial role in sustaining marine ecosystem balance. Accurate extraction of IWs information is essential for studying their properties. Most existing deep-learning-based internal wave extraction models rely heavily on large training datasets. However, large amounts of IWs synthetic aperture radar (SAR) images are difficult to access in practice. To address this issue, this article developed a generative adversarial network with multiscale downsampling and upsampling (GAN-MSDU) consisting of a series of adversarial networks at multiple scales. The model determines the scale through upsampling and downsampling procedures. Moreover, the adversarial network corresponding to each scale consists of a generator and a discriminator. The generator is designed to generate an IW as realistically as possible. The objective of the discriminator is to distinguish the generated image from the real data as much as possible. Through the design of multiscale architecture, this model successfully captures the characteristics of global and local IWs, and its adversarial training mode enhances the model's representational ability via the generated data. The main contributions are as follows: first, to address the scarcity of IW image data, this study proposes a framework that requires only four images as the training set, providing a novel approach to the problem of data scarcity. Second, to address the elongated features of IW crests, this study proposes a novel downsampling method that preserves the crest features during the downsampling process. As a result, the overall mean accuracy, F1-score, mean intersection over union, and FWIoU of the GAN-MSDU model are 99.48%, 42.64%, 63.77%, and 99.30%, respectively. The comparison and quantitative evaluation with other methods for small data problems show that the GAN-MSDU method is efficient and robust in IW extraction. |
| WOS关键词 | OCEANIC INTERNAL WAVES ; CLASSIFICATION ; SEGMENTATION |
| 资助项目 | National Natural Science Foundation of China[62161044] ; National Natural Science Foundation of China[62161045] ; Natural Science Foundation of Inner Mongolia[2023MS06003] ; Natural Science Foundation of Inner Mongolia[2022ZD05] ; Key Laboratory of Infinite-Dimensional Hamiltonian System and Its Algorithm Application (IMNU), Ministry of Education[2023KFYB06] ; Research Program of Inner Mongolia Normal University[CXJJS25026] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001573876600007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/203422]  |
| 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 Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Ctr Appl Math Sci, Key Lab Infinite Dimens Hamiltonian Syst & Its Al, Hohhot 010022, Inner Mongolia, Peoples R China 3.Inner Mongolia Normal Univ, Coll Math Sci, Hohhot 010022, Peoples R China |
| 推荐引用方式 GB/T 7714 | Saheya, B.,Ren, Xia,Gong, Maoguo,et al. IW Extraction From SAR Images Based on Generative Networks With Small Datasets[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:16473-16487. |
| APA | Saheya, B.,Ren, Xia,Gong, Maoguo,&Li, Xiaofeng.(2025).IW Extraction From SAR Images Based on Generative Networks With Small Datasets.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,16473-16487. |
| MLA | Saheya, B.,et al."IW Extraction From SAR Images Based on Generative Networks With Small Datasets".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):16473-16487. |
入库方式： OAI收割
来源：海洋研究所
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