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

• 作者: Bin Liu Bin Zhang Fan Wang Gang Zheng Le Gao Shuangshang Zhang Xiaofeng Li Yibin Ren Yingjie Liu Yuhai Liu
• 来源期刊: 国家科学评论（英文版）
• 年，卷（期）: 2020,（10）
• 所属期刊栏目: REVIEWS
• 页码范围: 1584-1605
• 页数: 22页
• 语种: 英文
• DOI: 10.1093/nsr/nwaa047

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 ofpetabytes 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.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Xiaofeng Li		1	0	0.0	0.0
2	Bin Liu		1	0	0.0	0.0
3	Gang Zheng		1	0	0.0	0.0
4	Yibin Ren		1	0	0.0	0.0
5	Shuangshang Zhang		1	0	0.0	0.0
6	Yingjie Liu		1	0	0.0	0.0
7	Le Gao		1	0	0.0	0.0
8	Yuhai Liu		1	0	0.0	0.0
9	Bin Zhang		1	0	0.0	0.0
10	Fan Wang		1	0	0.0	0.0