海洋内波是一种常见的致灾性中尺度海洋现象，因其对海洋军事和海洋工程等存在巨大威胁而被广泛关注。为了实现合成孔径雷达（SAR）图像海洋内波的准确检测，解决传统检测算法易受斑噪干扰的问题，本文提出了一种基于超像素分割和全局显著性特征的SAR海洋内波检测算法。首先，基于简单线性迭代聚类算法（SLIC）将SAR图像分割成特征均一的超像素；然后，利用超像素的梯度特征、灰度特征及空间特征构建内波显著性特征向量，计算其全局显著性并基于显著度提取内波超像素；最后，根据内波在傅立叶能量谱上的特征对内波区域和非内波区域进行标记并生成标签图像，用于对显著性检测结果进行校正。实验结果表明：本文方法对5景实验数据的内波条纹检测平均F1分数可达到0.884、平均虚警率为0.009，证明了本文方法在不降低SAR图像空间分辨率的条件下可以有效抑制斑噪的影响，实现高分辨率SAR海洋内波条纹的准确检测。

Objective: Ocean internal waves are a commonly observed catastrophic mesoscale oceanic phenomenon, which is of great attention due to its significant threat to the marine military and marine engineering. With the rapid development of science and technology, the ocean internal wave remote sensing detection method has attracted more and more attention. At present, remote sensing methods used for internal wave observation can be divided into synthetic aperture radar (SAR), visible light, infrared by frequency band. Among them, SAR has the advantages of all-day, all-weather and high-resolution, which is especially well-suited for remote sensing investigation of oceanic internal waves with frequent cloud coverage areas. In order to achieve accurate detection of ocean internal waves using synthetic aperture radar (SAR) images and to solve the problem that conventional detection algorithms are susceptible to SAR speckle noise interference, this paper proposes a SAR ocean internal wave detection algorithm based on superpixel segmentation and global saliency features. Method: Firstly, the SAR image is segmented into feature-uniform superpixels using the simple linear iterative clustering algorithm (SLIC). The SLIC algorithm combines neighboring pixels with similar features into superpixels. The superpixels not only enhance the continuity between the inner wave pixels, but also suppress the speckle noise interference. Then, the gradient feature, gray scale feature and spatial feature of the super-pixel are used to construct the internal wave saliency feature vector and calculate its global saliency. Based on the saliency, threshold segmentation algorithm is used to extract the internal wave superpixels. Experiments are conducted on GF-3 image and ERS-1 image, which show that the constructed internal wave saliency feature vector is beneficial to detect more internal wave stripes. Finally, the label image indicating the internal wave regions is generated according to the spectral characteristics of internal wave and used to correct the internal wave detection result in previous step. Result: We carried out the detection experiment of internal wave bright stripes on five SAR images with a resolution of about 10 meters. The experimental results show that the proposed method has good detection accuracy for these five high-resolution SAR internal wave images. The average F1 score of the internal wave detection for the five scene experimental data of our method could reach 0.884, and the average false alarm rate is 0.009. Conclusion: By comparing the internal wave detection results and related evaluation indexes of our method with the classical Canny operator and the deep learning U-Net method, the effectiveness and robustness of our proposed method in high-resolution SAR ocean internal wave detection are demonstrated, which is of great significance to improve the inversion accuracy of internal wave wavelength and amplitude.