目的 海洋涡旋识别研究已成为物理海洋领域的研究热点之一。传统的海洋涡旋识别算法多是基于物理参数法、流场几何特征法以及两种方法的混合，通过设置统一的阈值对海洋涡旋进行识别。在特定环境的海域中，传统算法的识别效果较好。但引起海洋涡旋生成的环境复杂多变且具有快速运动的特性，导致其具有快速连续变化的特点，依赖专家经验进行单一阈值设置的方法存在显著的主观性和不确定性，通常会导致海洋涡旋的漏判和错判，缺乏通用性。针对以上问题，为构建具有泛化能力的海洋涡旋自动识别方法，兼顾考虑海洋涡旋的全局和局部特征，提出一种复杂环境下海洋涡旋多特征融合识别方法。方法 首先通过数据预处理对数据集进行扩充；其次提取海洋涡旋的纹理特征（GLCM）、傅里叶描述子（FD）和Harris角点特征；接着将提取的GLCM特征进行均值化，并通过主成分分析法（PCA）对FD描述子和Harris角点特征进行降维；然后将处理后的特征向量进行串行融合；最后通过分类器完成对海洋涡旋的识别。结果 实验结果表明，提出的多特征融合方法的识别率高于单一特征方法的识别率，采用DT分类算法的识别精度最高，达86.904 5%；本文方法中采用PCA降维能有效提高识别精度，FD特征经PCA降维后，识别精度从83.906 0%提高到86.904 5%，Harris特征经PCA降维后，识别精度从84.009 7%提高到85.354 7%；且对于形态多样的海洋涡旋具有良好的鲁棒性。结论 本文融合了基于SAR影像海洋涡旋的3种特征信息，包括GLCM、FD和Harris角点特征，有效克服了阈值选取以及单一特征的不足，提高了基于SAR影像海洋涡旋的识别率，本文方法适用于复杂环境下的海洋涡旋识别。

Objective 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 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 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 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.