# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Overview of Traditional Methods of Wake Detection
#### 3.2.3 Other Methods  
Several other methods of wake detection have been developed throughout the years, mostly focusing on SAR products, due to their inherent advantages for maritime surveillance, but exploiting different methodologies and domains. Fast computation and rapid detection were the focus of [PERSON] et al. [50], [PERSON] et al. [51], and [PERSON] et al. [52]. [PERSON] et al. proposed a frequency-domain-based method that leverages the fast Fourier transform (FFT), achieving fast computation and low complexity for real-time ocean surveillance and practical SAR systems. Their approach capitalizes on the \"V\" pattern waves generated by moving ships, which are detectable by SAR. [PERSON] et al. [51] utilized a dynamic threshold and morphological filtering to binarize and clean the detection region, followed by a linear fitting method to confirm long ship wakes, with the final aim of a rapid detection method to identify long ship wakes in SAR images. The method leverages SAR imaging parameters and the ship's axial direction to pinpoint potential wake detection areas. The authors then tested their method on ENVISAT and GF-3 SAR data. Finally, [PERSON] et al. [52] proposed a method for SAR imagery using sparse regularization and a Cauchy prior. This method enhances linear features in the RT domain by employing the Cauchy proximal operator and the Moreau-Yoshida unadjusted Langevin algorithm (MYULA) for computational efficiency and robustness. They experimented with their method on imagery from COSMO-SkyMed. Sea state conditions and their impact on detection were analyzed in [53; 54]. An anomaly-detection-based method to enhance ship wake detection in SAR images across various sea states was the topic of [PERSON] et al. [53]. Traditional methods using RT and other methods based on detection of linear features operate best in conditions of calm seas, as the presence of excessive surface roughness and other sources of surface clutter can result in a significant increase in false detections. The method by [PERSON] et al. leverages image reconstruction errors from dictionaries trained on sea clutter images to identify anomalies. Ship wake detection was framed as an anomaly detection problem, where the reconstruction errors of ship wakes exceeded a certain threshold set by anomaly detectors. They utilized three anomaly detectors--isolation forest (iForest), local outlier factor (LOF), and one-class support vector machine (OCSVM)--to distinguish ship wakes from sea clutter. The issue of complex background conditions was also addressed by [PERSON] et al. [54], who proposed morphological component analysis (MCA) and dictionary learning to decompose the SAR image into a \"cartoon\" component with ship wakes and a sea-background texture component. The method adaptively learns separate dictionaries for these components and then uses a shearlet transform to enhance the ship wakes in the cartoon component. The enhanced wakes are detected using the principal component (PC) transform. Multi-image or multi-object detection has, finally, been the topic of other works, starting from [PERSON] [55], who explored the application of ship wake detection in SAR images for the Chinese Coast Guard's monitoring and enforcement capabilities. Their study reviewed several algorithms for ship wake detection, including two-parameter CFAR, K-distribution-based CFAR, multi-polarization detection, and multi-image correlation methods. [PERSON] et al. [56] proposed a method to detect multi-ship and multi-scale wakes in SAR products. Their method detects highlighted pixel areas and generates specific windows around centroids to identify wakes of varying sizes. It uses wake clustering to locate all wake components and statistical features to determine the visible length of wakes. Testing on Gaofen-3 SAR images demonstrated the effectiveness of the method. The approach involves a specific window search that reduces the detection area, utilizing a localized Radon-based enhancement algorithm to screen real ship targets and accurately locate wake axes.