Ship detection in reefs and deep-sea with medium-high resolution images
文献类型：期刊论文
| 作者 | Hong, Xiaorun5; Fu, Dongjie5; Tang, Jiasheng5; Lyne, Vincent4; Luo, Ming3; Su, Fenzhen2,5 |
| 刊名 | GEO-SPATIAL INFORMATION SCIENCE
 |
| 出版日期 | 2024-09-21 |
| 卷号 | N/A |
| 关键词 | Rotated-object detection medium-high resolution optical imagery SDGSAT-1 Sentinel-2 ship detection deep learning reefs deep-sea |
| DOI | 10.1080/10095020.2024.2404719 |
| 产权排序 | 1 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Accurate and efficient ship detection is crucial for ocean monitoring and management, especially in reefs and deep-sea, where fishing and illegal activities threaten sustainability of ecosystems. Obtaining the size of ships in reefs and deep-sea helps to identify ship types and to assess the impact of ships on marine ecosystems quantitatively. Ship detections were mainly applied at coast or river of the inland region using high spatial resolution remote sensing data due to its rich details and frequent coverage. However, the ships located in reefs and deep-sea regions were rarely studied because of price, and availability of high spatial resolution remote sensing data. Global covered medium-high resolution (10-30 m) remote sensing data (such as Sentinel-2) makes it possible to obtain the spatial-temporal distribution of ships, especially small ships. This study developed a deep learning ship detection algorithm - an enhanced Rotated-Ship Detector (RShipDet) to detect ships in reefs and deep-sea regions. RShipDet was applied to Nansha Islands based on two medium-high resolution ship detection datasets (Sentinel2-Ship and SDGSAT-Ship). These two datasets include various backgrounds of reefs and deep-sea, and complex scenarios such as cloud cover and parallel ships, adapting RShipDet to ship detection under complex circumstances in reefs and deep-sea. The results showed that: (1) Small gains: gains of 3.3% and 8.7% Average-Precision (AP) compared to Rotation-equivariant Detector (ReDet) and Faster-RCNN on Sentinel2-Ship dataset; (2) Strong generalization capabilities: 77.4% AP on SDGSAT-Ship dataset; (3) Better performance under complex conditions: RShipDet obtained more accurate ship detection results over regions with cloud cover, islands and reefs, deep-sea, and ports compared to other classical detectors. Our algorithm could be applied for better management of ocean resources and activities in reefs and deep-sea. |
| WOS关键词 | DATASET |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:001317582400001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207984]  |
| 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 | Fu, Dongjie |
| 作者单位 | 1.Commiss Geog Informat Sci, Int Geog Union, Beijing, Peoples R China 2.Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Peoples R China 3.Univ Tasmania, IMAS Hobart, Hobart, Tas, Australia 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hong, Xiaorun,Fu, Dongjie,Tang, Jiasheng,et al. Ship detection in reefs and deep-sea with medium-high resolution images[J]. GEO-SPATIAL INFORMATION SCIENCE,2024,N/A. |
| APA | Hong, Xiaorun,Fu, Dongjie,Tang, Jiasheng,Lyne, Vincent,Luo, Ming,&Su, Fenzhen.(2024).Ship detection in reefs and deep-sea with medium-high resolution images.GEO-SPATIAL INFORMATION SCIENCE,N/A. |
| MLA | Hong, Xiaorun,et al."Ship detection in reefs and deep-sea with medium-high resolution images".GEO-SPATIAL INFORMATION SCIENCE N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。