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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.