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...
Published in: | National Science Review |
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Main Authors: | , , , , , , , , , |
Format: | Report |
Language: | English |
Published: |
OXFORD UNIV PRESS
2020
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Subjects: | |
Online Access: | http://ir.qdio.ac.cn/handle/337002/169117 http://ir.qdio.ac.cn/handle/337002/169118 https://doi.org/10.1093/nsr/nwaa047 |
Summary: | 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. |
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