## 1 Introduction
** & **Method Category** & **Key Features** & **Advantages** & **Limitations** \\ \hline
[6] & Traditional image preprocessing & High-pass filtering method & Enhance tail feature; suppress background noise & Robustness insufficiency under complex sea conditions \\ \hline
[7] & Traditional image preprocessing & Decompose relative total variation technique & Enhance the visibility of the tail in complex backgrounds & High computational complexity, decomposition parameters need manual adjustment \\ \hline
[8] & Traditional image preprocessing & Image low-rank sparse decomposition & Separate noise from tail signal, reduce interference & Limited effect on low SNR images \\ \hline
[9] & Traditional image preprocessing & MAP estimation and GMC sparse regularization & Improve detection stability & High algorithm complexity and poor real-time performance \\ \hline
[10] & Traditional image preprocessing & FABEMD with Goldstein interferogram filter & Separate ocean features of different scales & Robustness insufficiency under complex sea conditions \\ \hline
[11] & Traditional image preprocessing & GMM and Radon transform & Enhances the visibility & Wakes with good visibility \\ \hline
[12] & Traditional image preprocessing & PWF and PDOF & enhance the wake detection performance & Only applicable to PolSAR \\ \hline
[13] & Traditional image preprocessing & Morphological component analysis & Separation of wake linear features and sea surface texture & Robustness insufficiency under complex sea conditions \\ \hline
[14] & Deep learning & CNN & Improved detection accuracy & Limited application scenarios \\ \hline
[15] & Deep learning & CNNs & Assessed the effectiveness & Robustness insufficiency under complex \\ \hline
[16] & Deep learning & Scattering model for simulating and YOLO5 & With significantly higher detection accuracy & Dependent on a large amount of labeled data, Radon domain noise still affects the parameter extraction \\ \hline
[17] & Deep learning & \begin{tabular}{c} Collaboration used its improvement method \\ \end{tabular}  & \ \begin{tabular}{c} Adapt to complex \\ scenarios \\ \end{tabular}  & \begin{tabular}{c} Dependent on a large amount \\ of labeled data, Radon domain noise still affects the parameter extraction \\ \end{tabular}  \\ \hline
[18] & Deep learning & \begin{tabular}{c} Trail simulation \\ and YOLO \\ \end{tabular}  & \ \begin{tabular}{c} Adapt to complex \\ scenarios \\ \end{tabular}  &
\begin{tabular}{c} Repeat the application of the application In principle, the accuracy of wake detection in the aforementioned methods is constrained by the sea surface background noise in SAR images and the linear feature intensity of wakes. Therefore, various preprocessing techniques, such as filtering, segmentation, and signal separation, are necessary for original SAR images to reduce background noise and enhance the linear feature of wakes, thereby increasing peak intensity in the transformed domain and improving wake detection effectiveness. Spaceborne SAR imaging involves the process of converting echo signals into images, with focusing processing performed in the azimuth direction using synthetic aperture time. The sea surface, as a random rough surface under marine wind conditions, is theoretically composed of large-scale approximately periodic waves overlaid with small-scale ripples and foam. Hence, in SAR images, the sea surface background presents a complex and dynamically changing random texture. However, unlike point targets, such as maritime vessels, the linear features of wakes are generally unaffected by the random motion of sea surface scattering points, which would otherwise result in a decrease in the azimuth resolution of the image pixels. Particularly, with high-resolution SAR employing sliding spotlight mode, the rotation of azimuth scanning angles brings about richer sea surface imaging information, which plays a crucial role in enhancing wake features during imaging processing.