Deep-learning-based information mining from ocean remote-sensing imagery

doi:10.1093/nsr/nwaa047

IOCAS-IR > 海洋环流与波动重点实验室

	Deep-learning-based information mining from ocean remote-sensing imagery
	Li, Xiaofeng 1,2; Liu, Bin 3; Zheng, Gang 4; Ren, Yibin 1,2; Zhang, Shuangshang 5; Liu, Yingjie 1,2; Gao, Le 1,2; Liu, Yuhai 1,6; Zhang, Bin 1,2; Wang, Fan1,2
	2020-10-01
发表期刊	NATIONAL SCIENCE REVIEW
ISSN	2095-5138
卷号	7 期号:10 页码:1584-1605
通讯作者	Wang, Fan(fwang@qdio.ac.cn)
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.
关键词	ocean remote sensing big data artificial intelligence image classification
DOI	10.1093/nsr/nwaa047
收录类别	SCI
语种	英语
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
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000588701300010
出版者	OXFORD UNIV PRESS
引用统计	被引频次：167[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/169118
专题	海洋环流与波动重点实验室
通讯作者	Wang, Fan
作者单位	1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 3.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China 4.Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China 5.Hohai Univ, Coll Oceanog, Nanjing 210098, Peoples R China 6.Dawning Int Informat Ind Co Ltd, Qingdao 266101, Peoples R China
第一作者单位	海洋环流与波动重点实验室; 中国科学院海洋大科学研究中心
通讯作者单位	海洋环流与波动重点实验室; 中国科学院海洋大科学研究中心
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.

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