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

• Main Authors: Li Xiaofeng, Liu Bin, Zheng Gang, Ren Yibin, Zhang Shuangshang, Liu Yingjie, Gao Le, Liu Yuhai, Zhang Bin, Wang Fan
• Format: Report
• Language: English
• Published: 2020
• Subjects: ocean remote sensing; big data; artificial intelligence; image classification; Sea ice
• Online Access: http://ir.qdio.ac.cn/handle/337002/175443

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.