Deep-learning-based information mining from ocean remote-sensing imagery
文献类型：期刊论文
| 作者 | Li, Xiaofeng3,4; Liu, Bin5; Zheng, Gang6; Ren, Yibin3,4; Zhang, Shuangshang1; Liu, Yingjie3,4; Gao, Le3,4; Liu, Yuhai2,3; Zhang, Bin3,4; Wang, Fan3,4
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| 刊名 | NATIONAL SCIENCE REVIEW
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| 出版日期 | 2020-10-01 |
| 卷号 | 7期号:10页码:1584-1605 |
| ISSN号 | 2095-5138 |
| 关键词 | ocean remote sensing big data artificial intelligence image classification |
| DOI | 10.1093/nsr/nwaa047 |
| 通讯作者 | Wang, Fan(fwang@qdio.ac.cn) |
| 英文摘要 | With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning-a powerful technology recently emerging in the machine-learning field-has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery. |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060101] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19090103] ; Key R&D Project of Shandong Province[2019JZZY010102] ; Key Deployment Project of Center for Ocean Mega-Science, CAS[COMS2019R02] ; CAS Program[Y9KY04101L] ; China Postdoctoral Science Foundation[2019M651474] ; China Postdoctoral Science Foundation[2019M662452] ; Senior User Project of RV KEXUE, by the Center for Ocean Mega-Science, Chinese Academy of Sciences[KEXUE2019GZ04] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| 出版者 | OXFORD UNIV PRESS |
| WOS记录号 | WOS:000588701300010 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/169117]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Wang, Fan |
| 作者单位 | 1.Hohai Univ, Coll Oceanog, Nanjing 210098, Peoples R China 2.Dawning Int Informat Ind Co Ltd, Qingdao 266101, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 5.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China 6.Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xiaofeng,Liu, Bin,Zheng, Gang,et al. Deep-learning-based information mining from ocean remote-sensing imagery[J]. NATIONAL SCIENCE REVIEW,2020,7(10):1584-1605. |
| APA | Li, Xiaofeng.,Liu, Bin.,Zheng, Gang.,Ren, Yibin.,Zhang, Shuangshang.,...&Wang, Fan.(2020).Deep-learning-based information mining from ocean remote-sensing imagery.NATIONAL SCIENCE REVIEW,7(10),1584-1605. |
| MLA | Li, Xiaofeng,et al."Deep-learning-based information mining from ocean remote-sensing imagery".NATIONAL SCIENCE REVIEW 7.10(2020):1584-1605. |
入库方式： OAI收割
来源：海洋研究所
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