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

National Science Review 2020年第10期7卷 1584-1605页

作者： Xiaofeng Li Bin Liu Gang Zheng Yibin Ren Shuangshang Zhang Yingjie Liu Le Gao Yuhai Liu Bin Zhang Fan Wang Key Laboratory of ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences Center for ocean Mega-Science Chinese Academy of Sciences College of Marine Sciences Shanghai Ocean University State Key Laboratory of Satellite ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources College of oceanography Hohai University Dawning International Information Industry Co. Ltd.

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.

关键词： ocean remote sensing big data artificial intelligence image classification

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Visualization of ocean remote sensing Information Based on IDL Object Graphics

Visualization of Ocean Remote Sensing Information Based on I...

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2010 International Conference on Computer and Communication Technologies in Agriculture Engineering (CCTAE 2010)

作者： Tao Ronghua Chen Biao Chen Xiaolu LiuCuihua Institute of remote sensing Navy Submarine Academy Qingdao China

Component-oriented programming technique and object-oriented graphics mode of IDL were applied in the two-dimensional distribution visualization of ocean remote sensing information according to the remote sensing inversion data. The methods of how to define an adaptive color table and set a contour object attribute were presented. The realization of ocean remote sensing information visualization containing various flags was discussed in detail. The example shows it can meet the fast display requirement of common ocean remote sensing information with excellent expansibility and flexibility.

关键词： IDL object graphics ocean remote sensing visualization

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Upper ocean responses to category 5 typhoon Megi in the western north Pacific

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Acta oceanologica Sinica 2012年第1期31卷 51-58页

作者： CHEN Xiaoyan PAN Delu HE Xianqiang BAI Yan WANG Difeng Department of Earth Sciences Zhejiang UniversityHangzhou 310027China State Key Laboratory of Satellite ocean Environment Dynamics Second Institute of OceanographyState Oceanic AdministrationHangzhou 310012China

Category 5 typhoon Megi was the most intense typhoon in 2010 of the world. It lingered in the South China Sea （SCS） for 5 d and caused a significant phytoplankton bloom detected by the satellite image. In this study, the authors investigated the ocean biological and physical responses to typhoon Megi by using chlorophyll-a （chl-a） concentration, sea surface temperature （SST）, sea surface height anomaly （SSHA）, sea surface wind measurements derived from different satellites and in situ data. The chl-a concentration （〉3 mg/m3） increased thirty times in the SCS after the typhoon passage in comparison with the mean level of October averaged from 2002 to 2009. With the relationship of wind stress curl and upwelling, the authors found that the speed of upwelling was over ten times during typhoon than pre-typhoon period. Moreover, the mixed layer deepened about 20 m. These reveal that the enhancement of chl-a concentration was triggered by strong vertical mixing and upwelling. Along the track of typhoon, the maximum sea surface cooling （6-8~C） took place in the SCS where the moving speed of typhoon was only 1.4-2.8 m/s and the mixed layer depth was about 20 m in pre-typhoon period. However, the SST drop at the east of the Philippines is only 1-2~C where the translation speed of typhoon was 5.5-6.9 m/s and the mixed layer depth was about 40 m in pre-typhoon period. So the extent of the SST drop was probably due to the moving speed of typhoon and the depth of the mixed layer. In addition, the region with the largest decline of the sea surface height anomaly can indicate the location where the maximum cooling occurs.

关键词： sea surface temperature chlorophyll-a concentration sea surface height anomaly upwelling typhoon Megi ocean remote sensing

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Detection of weak ship signals with the optimization of polarimetric contrast enhancement

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High Technology Letters 2008年第1期14卷 85-91页

作者：李海艳 He Yijun Institute of oceanology Chinese Academy of Sciences Qingdao 266071 P.R. China Graduate University of Chinese Academy of Sciences Beijing 100049 P.R. China

An optimization of polarimetric contrast enhancement method is proposed to detect ships with lowship-to-clutter power *** received power is calculated with Kennaugh matrix and an iterative algo-rithm is adopted to get the optimal polarimetric *** optimization method depresses the power of o-cean clutter and increases the power of ship *** the double effects,the contrast of ship to oceanis dramatically *** small ship or weak signals of low ship-to-ocean power ratio can easily *** signals can be distinguished from speckle noise using the different variation trend after op-timization,and thus the threshold problem can be ***,the analyses of different ship'sKennaugh matrices give two *** is that the results are affected little by choosing differentKennaugh matrices of ships with strong intensity from Synthetic Aperture Radar(SAR)*** otheris that ship's Kennaugh matrix chosen from real SAR images is more favorable than that of ideal ***,the optimization results are confirmed by polarimetric scattering angle and co-polarizationphase difference.

关键词： polarimetric SAR ship detection optimization ocean remote sensing

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ocean Observation from Haiyang Satellites

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空间科学学报 2020年第5期40卷 898-907页

作者： LIN Mingsen JIANG Xingwei National Satellite ocean Application Service Beijing 100081

In 2018,China successfully launched three new Haiyang(which means ocean in Chinese,referred to as HY)satellites which are an ocean color observation satellite HY-1C(operational),an ocean dynamics environment satellite HY-2B(operational)and the China-France ocean satellite CFOSAT(experimental).In 2019,all the three satellites had finished their commissioning phases and were declared ***-2A satellite continues to operate in-orbit,and its operational status is basically *** in 2020,China has 4 Haiyang satellites in-orbit,China’s ocean satellites enter into a new operational application *** operation of the ground application system of Chinese ocean satellites is *** 2019,Beijing,Hainan,Mudanjiang,and Hangzhou ocean satellite ground stations had received the data of HY-1C,HY-2A,HY-2B,and CFOSAT 5012 orbits and 26.46 TB data had been distributed to both domestic and international *** ocean satellite data has played an important role in marine disaster prevention and mitigation,development and management of marine resources,maintenance of marine rights and interests,marine environment protection,scientific researches,and blue economy development.

关键词： HY-1C HY-2B CFOSAT ocean observation satellite ocean remote sensing Satellite ocean applications

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Geological environment in the South China Sea

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Journal of oceanology and Limnology 2023年第2期41卷 403-408页

作者： Danling TANG Zhen SUN Guangjun SUI Guangdong remote sensing Center for Marine Ecology and Environment Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)Guangzhou 511458ChinaSouth China Sea Institute of OceanographyChinese Academy of SciencesGuangzhou 511458China South China Sea Institute of oceanography Chinese Academy of SciencesChinaSouthern Marine Science and Engineering Guangdong Laboratory(Guangzhou)Guangzhou 511458China Guangdong University of Foreign Studies Guangzhou 510420China

The South China Sea(SCS),situated in southern China,at the junction of the Pacific Plate,the Eurasian Plate,and the Indian ocean Plate,is a northeast-southwest trending semi-enclosed *** spans an area of approximately 3.5 million square kilometers and has an average water depth of about 1200 m,its deepest point reaching 5559 *** 2021,a scientific expedition(called as U1 voyage)in the South China Sea was organized by the Innovation Research Team of Guangdong Special Key Program from March to April,this marks the first comprehensive scientific research voyage to the southern Uboundary *** of a total of 30 papers,this special issue is to share a portion of the research findings from this scientific expedition U1 voyage,covering six aspects:1)characteristics of the marine ecosystem in the SCS and its response to marine dynamic processes;2)multi-scale marine dynamic processes,sea-air interactions,and forecasting techniques in the SCS;3)geomorphology and geological structure;4)sedimentary processes and resource potential in the SCS;5)geostrategy,rights and interests maintenance and strategic countermeasures in the SCS;6)marine scientific *** integrating the scientific research with the study of history,jurisprudence and international strategies,this issue presents new insights into the formation history and scope evolution of the SCS,and it also seeks to establish a new scientific framework based on the marine governance and development of the SCS.

关键词： South China Sea U-boundary geological science U 1 voyage ecological environment ocean remote sensing

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