Objective

2

Ocean eddy recognition has become one of the hotspots in the field of physical oceanography research. The rapid and continuous change of ocean eddies brings great challenges to their accurate recognition research. On the one hand

the ocean environment that causes the ocean eddies is complex and variable. On the other hand

ocean eddies undergo rapid and continuous change

which cause dramatic changes in the morphological structure and motion state during their movement. With the development of high-aging and large-scale Earth observation technology

it provides a rich data foundation for the research on the rapid and continuous change of ocean eddies

which greatly promotes research on ocean eddy recognition. However

traditional manual visual recognition methods have significant limitations. It is an impossible task to artificially recognize ocean eddies one by one in a large data set

and the manual visual recognition of ocean eddies is influenced by subjective judgment. Traditional methods have significant uncertainty in ocean eddy recognition results due to subjective difference

which form unstatistical errors. Therefore

the use of computer technology to automatically recognize ocean eddies is important. Ocean eddies leads the ocean water to gather or scatter

causing obvious changes on surface roughness. The irregular spiral structure in SAR images shows bright or dark stripes. It has rich texture features and contour

which provides an abundant feature of the ocean eddy recognition information. Traditional ocean eddy recognition methods are mainly based on the physical parameters

vector geometry

or the hybrid of these two methods with specific threshold value. These methods can achieve good results in certain ocean areas. The selection of threshold is mainly influence the recognition accuracy of the traditional method largely. In addition

the morphological structures of ocean eddies greatly vary under different ocean states. Moreover

the complex and variable environment causes the rapid and continuous change of ocean eddies. Therefore

it is difficult to determine the suitable threshold in traditional method. Methods that set thresholds based on expert knowledge are subjective and uncertain

which often lead to omission

misjudgments

and lack of generality. To solve these problems

we propose an automatic ocean eddy recognition method with generalization ability based on multi-feature fusion in complex environment.

Method

2

Our method includes data-preprocessing

feature extraction

multi-feature fusion

and classifier training. First

the dataset

including randomly clip

scale transform

and rotation transform

is extended through data-preprocessing to improve the robustness of our method. We derived our data set from SAR images generated by the ENVISAT and ERS-2 satellites between 2005 and 2010. In this paper

136 SAR images with and without ocean eddies are included in the data sets. In actual applications

the construction of large-scale data sets requires high labor costs

especially the construction of ocean eddy data sets. Moreover

it has high requirements for data set builders

and the difficulty of construction the data set is intensified. Adequate and diverse data sets are the key to the recognition algorithms in the field of image recognition. Therefore

we use the data augmentation method to extend the data set before the ocean eddy recognition method. Second

the gray-level co-occurrence matrix (GLCM)

Fourier descriptors (FD)

and Harris corners features are extracted. Ocean eddies in SAR images have abundant features

such as shape

texture

and color characteristics

but a single feature is not enough to accurately recognize them because of complicated ocean state

weather changes

equipment disturbance

and various interference. GLCM can represent the comprehensive information of the image on the direction

interval

change range

and speed. FD is related to the size

direction

and starting point of the shape according to the properties of the Fourier transform. In areas with rich texture information

the Harris operator can extract many useful feature points

whereas in areas with less texture information

fewer feature points are extracted. It is a relatively stable point feature extraction operator. Then

we extract the averaged GLCM feature and utilize principal component analysis (PCA) to reduce the features dimensions of FD descriptors and Harris corners. In the calculation of the GLCM feature

the value of the direction considers four conditions at 0°

45°

90°

135°

representing east-west

northeast-southwest

south-north

and southeast-northwest

respectively. The dimensions of the FD and Harris are reduced using PCA

because their features are too high

the learning time of classifier will be very long

and the classification ability is decreased. Third

the processed feature vectors are serial fused. Finally

the recognition of ocean eddies is achieved by the classifier. In this paper

10-fold cross-validation is used to test the accuracy of the algorithm. Simultaneously

three typical classification algorithms are adopted including support vector machine)

decision tree (DT)

and multi-layer perceptron.

Result

2

The results indicate that the accuracy of the proposed method based on multi-feature is higher than that based on single feature methods

and the recognition accuracy of the DT classification algorithm is the highest

reaching 86.904 5%. The PCA dimensionality reduction method can effectively improve the recognition accuracy. The FD and Harris feature dimension are reduced by PCA

and their recognition accuracy is improved from 83.906 0% to 86.904 5% and 84.009 7% to 85.354 7%

respectively. Moreover

their robustness to a variety of morphological changes of ocean eddies is good.

Conclusion

2

This method can be used to distinguish whether the SAR images have ocean eddies. It fuses three kinds of image features

including GLCM

FD

and Harris corners

which effectively overcomes the shortcomings of traditional methods based on threshold setting and single feature and improves the recognition accuracy of ocean eddies based on SAR images. Hence

multi-feature fusion improves the recognition accuracy to a certain extent compared with single-feature recognition. Our method is suitable for the recognition of ocean eddies in complex environment and has generality.