Deep-learning-based information mining from ocean remote-sensing imagery
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 an...
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ftpubmed:oai:pubmedcentral.nih.gov:8288802 2023-05-15T18:18:27+02:00 Deep-learning-based information mining from ocean remote-sensing imagery Li, Xiaofeng Liu, Bin Zheng, Gang Ren, Yibin Zhang, Shuangshang Liu, Yingjie Gao, Le Liu, Yuhai Zhang, Bin Wang, Fan 2020-03-19 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288802/ https://doi.org/10.1093/nsr/nwaa047 en eng Oxford University Press http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288802/ http://dx.doi.org/10.1093/nsr/nwaa047 © The Author(s) 2020. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. CC-BY Natl Sci Rev Review Text 2020 ftpubmed https://doi.org/10.1093/nsr/nwaa047 2021-10-24T00:22:36Z 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. Text Sea ice PubMed Central (PMC) National Science Review 7 10 1584 1605 |
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Review Li, Xiaofeng Liu, Bin Zheng, Gang Ren, Yibin Zhang, Shuangshang Liu, Yingjie Gao, Le Liu, Yuhai Zhang, Bin Wang, Fan Deep-learning-based information mining from ocean remote-sensing imagery |
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Review |
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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. |
format |
Text |
author |
Li, Xiaofeng Liu, Bin Zheng, Gang Ren, Yibin Zhang, Shuangshang Liu, Yingjie Gao, Le Liu, Yuhai Zhang, Bin Wang, Fan |
author_facet |
Li, Xiaofeng Liu, Bin Zheng, Gang Ren, Yibin Zhang, Shuangshang Liu, Yingjie Gao, Le Liu, Yuhai Zhang, Bin Wang, Fan |
author_sort |
Li, Xiaofeng |
title |
Deep-learning-based information mining from ocean remote-sensing imagery |
title_short |
Deep-learning-based information mining from ocean remote-sensing imagery |
title_full |
Deep-learning-based information mining from ocean remote-sensing imagery |
title_fullStr |
Deep-learning-based information mining from ocean remote-sensing imagery |
title_full_unstemmed |
Deep-learning-based information mining from ocean remote-sensing imagery |
title_sort |
deep-learning-based information mining from ocean remote-sensing imagery |
publisher |
Oxford University Press |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288802/ https://doi.org/10.1093/nsr/nwaa047 |
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Sea ice |
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Sea ice |
op_source |
Natl Sci Rev |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8288802/ http://dx.doi.org/10.1093/nsr/nwaa047 |
op_rights |
© The Author(s) 2020. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
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CC-BY |
op_doi |
https://doi.org/10.1093/nsr/nwaa047 |
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National Science Review |
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