The X-band navigation radar offers advantages such as high resolution, low attenuation, and a wide detection range, which makes it suitable for wave parameter inversion. Compared to traditional methods, deep learning approaches can uncover overlooked factors and effectively address uncertainties inherent in conventional inversion techniques. However, X-band radar images annotated with wave parameters face challenges including high acquisition costs, limited high sea state samples, and significant noise interference, which leads to suboptimal performance of deep learning models in predicting wave parameters. To address these issues, our research focuses on enhancing X-band radar image processing. By identifying and mitigating target interference in X-band radar images, we obtain high-quality sample data, expand the database capacity, and enhance the accuracy and generalization capabilities of deep neural networks. Consequently, this improves the accuracy of wave parameter inversion results.

An improved region growing method is adopted for processing target interference in X-band radar images, which is primarily divided into two sections. The first part involves screening the areas of target interference. Initially, based on the imaging characteristics of bright areas in concentrated target interference, it determines whether there exists a potential interference area of pseudo-target in the X-band radar image. Upon identifying such areas, the seed growth point is determined for the interference area of the pseudo-target. Subsequently, the region growing method is employed to delineate the interference area of the pseudo-target. The termination conditions signal the completion of the growth process. Following final growth, the judgment of whether the grown area constitutes the interference area of the pseudo-target is made based on indicators such as average gradient, which confirms it before proceeding to the second part. The second part involves compensating for the interference area of the target, which involves four steps: image pre-compensation, image expansion, four-point mean filling, and smooth transition. Through these steps, the actual wave texture image of the sea area where the target object interferes is restored as accurately as possible, thus providing high-quality images for a deep convolutional neural network-based wave parameter inversion model.

The improved region growing method can effectively identify the interference area of the target (Fig. 4). The added repair module can, to some extent, restore the sea clutter texture features in the area where the target object interference occurs (Fig. 5). The image processing results of this method under different sea conditions in three areas meet the requirements for improving image quality (Fig. 6). The relative error is generally lower between the processed image neural network model inversion results and the standard value (Fig. 10). The processed image is more conducive to improving the accuracy of the neural network model inversion (Table 3).

An X-band radar image processing method based on an improved region growing method is proposed to address the interference of targets in radar sea surface echo images. By recognizing and restoring target objects, the method aims to enhance radar sea surface echo images, rendering them with clear wave textures devoid of target objects. The processed X-band radar images are utilized for deep convolutional neural network inversion of wave parameters. The inversion results demonstrate a reduction of 43% in the average relative error of significant wave height, a 37% decrease in root mean square error, and a 2% increase in the correlation coefficient. These findings validate the method’s capability to significantly improve the accuracy of wave parameter inversion.