In previous research, the identification of the Bragg region, which is often achieved using image recognition, was easily affected by ionospheric interference and different manual parameter settings. Strong disturbances from the ionosphere and other environmental noise may interfere with high-frequency radar (HFR) systems when determining first-order Bragg regions and thus may directly influence the surface current mapping performance. To avoid human intervention in first-order Bragg-region recognition, deep learning methods were used to extract multiple levels of feature abstractions of the first-order Bragg region. In this study, to assist in developing sufficient training data, a procedure integrating a morphological approach and Otsu’s method was proposed to provide manual labelled data for a U-Net deep learning model. Additionally, several sets of activation functions and optimizers were used to achieve optimal deep learning model performance. The best combination of model parameters results in an accuracy of over 90%, an F1 score of over 80%, and an intersection over union (IoU) reaching 60%. The identification time of one image is approximately 70 ms. These results demonstrate that this deep learning model can predict the position of first-order Bragg regions under ionospheric interference and avoid being affected by strong noise that could cause prediction errors. Hence, deep learning and image fusion processing can effectively recognize the first-order Bragg regions under strong interference and noise and can thereby improve the surface current mapping accuracy.
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