Objective

2

Mesoscale eddy analysis is developed in the field of world's oceans and marginal seas. The multi-scale response mechanism and feedback interaction of mesoscale eddy and ocean phenomena has been promoted through mesoscale eddy generation

growth and extinction

the large scale spatial mesoscale eddy is essential to the issues of transport and distribution of global marine contexts of energy

heat and fresh water in terms of the feature of irregular three-dimensional structure and multifaceted dynamics. Therefore

the automatic detection of ocean mesoscale eddy can be used to understand the evolution of ocean mesoscale eddy and the role of ocean mesoscale eddy in material transport and energy transfer. The traditional ocean mesoscale eddy detection methods are mostly adopted based on the physical parameter

flow field geometric feature or both of them. The mesoscale eddies are detected by setting appropriate threshold artificially. The emerging deep learning technology is beneficial to ocean mesoscale eddy detection methods based on deep network model. A classifier is used to classify mesoscale eddy based on manual design and features extraction. The traditional algorithm has good mesoscale eddy detection effect via horizontal bounding box. However

the marine scenario of mesoscale eddy is complicated and ocean mesoscale eddy has the feature of irregular shape

complicated structure and length-width ratio uncertainty and dense distribution. The traditional threshold-dependent detection algorithm has constrained of its rigid and poor generalization ability. The horizontal boundary box methods have redundant horizontal detection region

nested detection box and overlapped issue. Therefore

we propose an automatic detection method of ocean mesoscale eddy based on multi-scale rotating anchor mechanism.

Method

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We visualize and normalize the satellite remote sensing sea level height anomaly data. The visual dataset is annotated based on ocean mesoscale eddy understanding and global daily ocean eddy satellite altimeter data. To expand small number of accurate expert annotation data sets

we use two-dimensional image processing strategy to construct the training set

such as adding noise and clipping operation. The basic features are derived of the factors of scale and signal noise ratio. We construct an automatic mesoscale eddy detection model based on multi-scale rotating anchor mechanism and one-stage object detection network RetinaNet. In the network structure

ResNet backbone network extract mesoscale eddy feature through introducing residual mapping and jump connection into the network. The feature information of the obtained residual block is added into the following residual block. It avoids the problem of gradient disappearance and deeper feature information loss effectively. At the same time

we use the feature pyramid network structure to integrate the low-level and high-level features of mesoscale eddy. It provides spatial and high-level semantic feature information of complex structure

different scales and more rapid sustainable changes. To cover the multi-oriented mesoscale eddy object with arbitrary shapes

it is generated that multi-scale rotation detection anchors are composed of scale

multi-aspect ratio and multi-rotation angles for each grid point of the output feature map. We integrate the classification and regression subnetworks

in which the classification subnetwork predict the probability of the presence of the object spatially

and the regression subnetwork predict location information via the regression transformation of predictive bounding box and the generated anchor. Finally

non-maximum suppression is used to obtain the final detection result.

Result

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Thanks to multi-scale rotating anchor mechanism

this experiment shows that our ocean mesoscale eddy automatic detection method can improve the issues of low detection precision

detect box nesting and overlapping via large aspect ratio of ocean mesoscale eddy

the deductible area of horizontal detection. It can detect the existence of multi-core eddy effectively. Furthermore

comparative analysis is conducted in same data set between feature pyramid networks (FPNs)

faster region-convolutional neural networks (Faster R-CNNs) and ours. It shows that the optimal detection accuracy can reach 90.22%

which is 8% higher than the horizontal bounding box detection method

and there is not much difference in speed. Then

we evaluate the accuracy

recall and F1 value amongst the Indian Ocean

Pacific Ocean and Atlantic Ocean. At the same time

the center and scale information of mesoscale eddy can be obtained based on the output position information of the boundary box. Finally

we carried out an empirical analysis of the rotation detection of oceanic mesoscale eddies in the Indian Ocean

Pacific Ocean

Atlantic Ocean and equatorial waters

which illustrates that the model has its potential generalization ability.

Conclusion

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Our research is focused on detection redundancy

overlapping and nested detection boxes in the horizontal bounding box detection method. We facilitate a deep network model based on a one-stage target detection network RetinaNet and promote the generation mechanism of multi-scaled rotating anchors via multifaceted ratio and multi-rotation

and the integrated classification and regression subnetworks. Non-maximum suppression is used to obtain the final detection results. The features of mesoscale eddy are demonstrated based on irregular shape

complex and variable structure

uncertain ratio and dense distribution

The redundancy

overlap and nesting barriers of the horizontal detection box region are released effectively and the existence of multi-core eddy can be detected. Our method improves detection accuracy and guarantee the detection speed compared to the popular object detection methods in recent three years. We evaluated the experimental effectiveness of data augmentation on the original dataset

the noise-added dataset

and the noise-trimmed dataset. We carry out the accuracy

recall and F1 value analysis based on multiple oceans like the Indian Ocean

Pacific Ocean and Atlantic Ocean

which demonstrates the generalization ability of the model.