It is aware that big data has gathered tremendous attentions from academic research institutes, governments, and enterprises in all aspects of information sciences. With the development of diversity of marine data acquisition techniques, marine data grow exponentially in last decade, which formsmarine big data. As an innovation, marine big data is a double-edged sword. On the one hand, there are many potential and highly useful values hidden in the huge volume of marine data, which is widely used in marine-related fields, such as tsunami and red-tide warning, prevention, and forecasting, disaster inversion, and visualization modeling after disasters. There is no doubt that the future competitions in marine sciences and technologies will surely converge into the marine data explorations. On the other hand, marine big data also brings about many new challenges in data management, such as the difficulties in data capture, storage, analysis, and applications, as well as data quality control and data security. To highlight theoretical methodologies and practical applications of marine big data, this paper illustrates a broad view about marine big data and its management, makes a survey on key methods and models, introduces an engineering instance that demonstrates the management architecture, and discusses the existing challenges.
Automatic recognition of ocean eddies has become one of the hotspots in the field of physical oceanography. Traditional methods based on either physical parameters or geometry features require manual parameter adjustment, and cannot adapt to the dynamic changes of ocean eddies caused by complicated ocean environments. To address these issues, we propose a new eddy recognition method in SAR images with adaptive weighted multi-feature fusion. Specially, to better characterize eddies, we first extract texture, shape and corner features using global Gray Level Co-occurrence Matrix (GLCM), detailed Fourier Descriptor (FD) and local salient Harris features respectively. Secondly, considering the different importance of features for eddy recognition, we propose an adaptive weighted feature fusion method with multiple kernel learning (MKL). Here, a combined kernel is derived to fuse three selected kernels for the three types of features with the weights trained by MKL. Finally, we design a SVM classifier with the combined kernel to realize the eddy recognition. The experimental results show that: 1) our proposed method can reach 93.42% of eddy recognition accuracy, which is much higher than the methods with only one single feature; 2) adaptive weighted fusion plays an important role in improving the accuracy. Our proposed method with MKL gains a 8.36% accuracy increase than the method without MKL. Through adaptive weighted fusion, our method avoids the manual parameter adjustment and is more robust and general. Experimental results have proven that our method is effective and applicable to recognize eddies.INDEX TERMS Multi-feature fusion, adaptive weighted fusion, multiple kernel learning, ocean eddies, image recognition, SAR images.
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