Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images
文献类型：期刊论文
| 作者 | Li, Xuemei3; Wang, Xing4; Ye, Huping1,5; Qiu, Shi6; Liao, Xiaohan2,5 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 62页码:4208312 |
| 关键词 | Sea measurements Image segmentation Feature extraction Remote sensing Transformers Training Generators Coastal line generative adversarial network (GAN) remote sensing segmentation transformer Unet |
| DOI | 10.1109/TGRS.2024.3435963 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | The demarcation between the sea and the land, commonly referred to as the coastline, is of paramount importance for the dynamic monitoring of its alterations. This monitoring is essential for the effective utilization of marine resources and the conservation of the ecological environment. Addressing the challenges posed by the extensive expanse of coastal lines, which can complicate their acquisition and processing, this study utilizes remote sensing imagery to introduce an algorithm for coastal line segmentation. The algorithm integrates multiple networks to enhance its effectiveness. Innovations encompass the development of an extraction algorithm for coastal lines that are as follows. First, utilize an attention-guided conditional generative adversarial network (AC-GAN) model, which redefines the task of image segmentation by framing it as a style transformation problem. Second, a strategy for coastal line segmentation utilizes Dense Swin Transformer Unet (DSTUnet) to construct a densely structured model. This approach integrates Transformer to prioritize focal regions, thereby enhancing image and semantic interpretation. Third, a transfer learning framework is proposed to integrate multiple features, leveraging the strengths of different networks to achieve accurate segmentation of coastal lines. The study introduced two datasets, and the experimental results confirm that parallel network configurations and asymmetric weighting are superior in achieving optimal results, with an area overlap measure (AOM) score of 85%, outperforming the Unet by 5%. |
| WOS关键词 | COASTLINE EXTRACTION ; NETWORK |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001288457800005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206953]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Xing; Ye, Huping |
| 作者单位 | 1.Chinese Acad Sci, Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China 3.Chengdu Univ Technol, Sch Mech & Elect Engn, Chengdu 610059, Peoples R China 4.Natl Inst Measurement & Testing Technol, Elect Res Inst, Chengdu 610021, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 6.Xian Inst Opt & Precis Mech, Chinese Acad Sci, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xuemei,Wang, Xing,Ye, Huping,et al. Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:4208312. |
| APA | Li, Xuemei,Wang, Xing,Ye, Huping,Qiu, Shi,&Liao, Xiaohan.(2024).Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,4208312. |
| MLA | Li, Xuemei,et al."Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):4208312. |
入库方式： OAI收割
来源：地理科学与资源研究所
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