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

National science review, 2020-10, Vol.7 (10), p.1584-1605 [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. ;The Author(s) 2020. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. 2020 ;ISSN: 2095-5138 ;EISSN: 2053-714X ;DOI: 10.1093/nsr/nwaa047 ;PMID: 34691490

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  • Title:
    Deep-learning-based information mining from ocean remote-sensing imagery
  • Author: Li, Xiaofeng ; Liu, Bin ; Zheng, Gang ; Ren, Yibin ; Zhang, Shuangshang ; Liu, Yingjie ; Gao, Le ; Liu, Yuhai ; Zhang, Bin ; Wang, Fan
  • Subjects: Review
  • Is Part Of: National science review, 2020-10, Vol.7 (10), p.1584-1605
  • Description: 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.
  • Publisher: China: Oxford University Press
  • Language: English
  • Identifier: ISSN: 2095-5138
    EISSN: 2053-714X
    DOI: 10.1093/nsr/nwaa047
    PMID: 34691490
  • Source: PubMed Central
    DOAJ Directory of Open Access Journals
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