# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 4 Discussion  
The main problems that every ship wake detection method has to face are as follows:  
* Several sources of clutter are present on the marine surface, which can cause a higher rate of false positives. Furthermore, these sources of clutter vary greatly in their appearance [24; 96] and can be difficult to filter out;
* The quality of wake imaging is strongly dependent on local and, often, global meteor marine conditions. Even with SAR, which is heavily researched due to its all-weather all-time capabilities, the visualization of wake structures is influenced by wind, sea, and local atmospheric conditions;
* The mechanism behind wake generation is not consistent at a high-level analysis, and real images often feature different combinations of wake structures, if any at all;
* Wake detection near coasts and low depth areas is, in general, particularly problematic and requires different considerations. Very few works have focused on wake detection in these conditions;
* Ship wake detection is limited to moving ships. Table 3 contains an overview of the methods cited in this review. As previously stated, applications on SAR imagery have been investigated more thoroughly than optical imagery. While traditional methods have been investigated for at least three decades, DL-based techniques are newer and growing in number. Publications referenced in this review regarding ship wake visualization or detection in airborne or spaceborne remote sensing images were not included in the table if they did not propose and test a solution to tackle the task. Figure 6 contains a timeline of referenced publications involving wake detection methods up to April 2024. The trend shows an increased interest in the topic in the last decade, and the recent increase in DL-based solutions. Nonetheless, traditional methods still prove of great interest to the scientific community. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & **Type** & **Method** & **Advantages** & **Disadvantages** & **Imagery Source** \\ \hline
[64] & DL, Optical & \begin{tabular}{c} Keypoints method \\ using deep learning \\ for wake recognition \\ \end{tabular} & \begin{tabular}{c} High accuracy; fast \\ processing; \\ lightweight model for \\ edge devices \\ \end{tabular} & \
\begin{tabular}{c} Struggles under poor \\ conditions; limited to \\ optical images \\ \end{tabular} &
\begin{tabular}{c} Sentinel-2, \\ Landsat-9 \\ \end{tabular} \\ \hline
[8] & DL, SAR & \begin{tabular}{c} Lighter version of \\ YOLOv4 for \\ efficiency; detects \\ ships and wakes \\ \end{tabular} & \
\begin{tabular}{c} Novel method; more \\ efficient than classic \\ YOLOv4 \\ \end{tabular} &
\begin{tabular}{c} Small dataset used in \\ study; may limit \\ generalizability. \\ \end{tabular} & Gaofen-3 \\ \hline
[11] & DL, Optical/Synthetic & \begin{tabular}{c} Kelvin wake \\ detection from \\ large-scale optical \\ imagery using \\ simulated data \\ trained deep neural \\ network \\ \end{tabular} & \
\begin{tabular}{c} High accuracy (Recall \\ 94\%, Precision 70.8\%), \\ effective use of \\ simulated data to \\ address data scarcity \\ \end{tabular} &
\begin{tabular}{c} Moderate precision, \\ requires synthetic \\ data to compensate \\ for limited real wake \\ samples \\ \end{tabular} & Gaofen-1 \\ \hline
[65] & DL, SAR & \begin{tabular}{c} Presented \\ OpenSARWake \\ dataset. Tested \\ several architectures \\ on it. Feature \\ Refinement Oriented \\ Detector (SWNet) \\ \end{tabular} & \begin{tabular}{c} Large-scale SAR \\ dataset, multi-band \\ data, diverse wake \\ types \\ \end{tabular} & \
\begin{tabular}{c} Moderate mAP, \\ challenges with \\ complex sea clutter \\ and small targets in \\ SAR images \\ \end{tabular} &
\begin{tabular}{c} Sentinel-1A, \\ TerraSAR-X, \\ ALOS-PALSAR \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._Of the traditional methods, those based on RT and other linear feature extraction methods are the most common.