## 1 Introduction  
Synthetic aperture radar (SAR) offers all-weather, day-and-night monitoring capabilities for large oceanic areas, enabling the active imaging of critical maritime zones. These features make SAR a vital remote sensing tool for marine target surveillance [1]. There are two approaches for detecting maritime vessels using spaceborne SAR: one is detecting the vessels themselves, and the other is detecting their wakes [2]. The detection and classification of maritime vessel targets have been a global research priority. However, in high sea states, amplified ocean background noise and vessel motion complexity often cause defocusing or positional displacement in SAR images, leading to target detection failures. Due to the wide coverage, distinct features, and prolonged duration of vessel wakes, wake-based target detection offers advantages over the direct detection of vessel targets in images, particularly in the discovery of weak targets and the acquisition of vessel motion information, thus holding broader application value for maritime security monitoring. The wakes generated by vessel motion on the sea surface typically exhibit distinct geometric characteristics, manifesting various phenomena, such as turbulent wakes, Kelvin wakes, and internal wave wakes [3]. For instance, by obtaining the width and direction of turbulent wakes, one can infer the vessel's width and heading; estimating the wavelength and propagation direction of Kelvin wakes can provide insights into the vessel's speed and heading, while internal wave wakes also contain characteristic parameters of maritime or subsea moving targets. In certain scenarios, vessel wakes can assist in detecting weak maritime or subsea targets that are difficult to detect in images [4]. In 2022, [PERSON] presented a synthetic SAR imagery dataset with ship wakes and used the deep learning (DL) method to classify ships. The results show that ship detection and recognition accuracy can be effectively improved [5]. The integration and information fusion of vessel and wake detection targets can effectively enhance the reliability and efficiency of spaceborne SAR in maritime surveillance, holding significant importance for maritime safety and ocean environmental protection. Research on ship wake detection is primarily based on traditional methods, which mainly rely on the image characteristics of wakes and transform the problem into a line feature detection issue under speckle noise. Such methods typically require denoising of the original SAR image, followed by the detection of lines using Radon or Hough transforms to obtain wake information. Numerous improved algorithms have emerged to enhance the accuracy of wake detection. [PERSON] et al. combined high-pass filtering with Radon transforms to detect wakes in SEASET images, achieving favorable results [6]. In 2017, [PERSON] et al. utilized relative total variation techniques to decompose and reconstruct images, detecting wake images using Radon transforms, which can be applied to ship wake detection in complex background SAR images [7]. [PERSON] et al. investigated ship wake detection by preprocessing original SAR images with low-rank sparse decomposition and using local Radon transforms [8]. In 2019, [PERSON] et al. reconstructed wakes in the Radon domain using MAP estimation and GMC sparse regularization to overcome various interferences in SAR images [9]. Some scholars have achieved the decomposition and feature enhancement of Kelvin wakes, turbulent wakes, and other medium/large-scale oceanic features in images by employing the fast and adaptive bidimensional empirical mode decomposition (FABEMD) method along with a modified Goldstein interferogram filter, demonstrating high efficiency and strong applicability [10]. In 2024, [PERSON] introduced a novel approach for detecting ship wakes using a Gaussian mixture model (GMM) and Radon transform, which enhances the visibility of linear features. The method is successful in achieving greater wake detection accuracy [11].