# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 5 Conclusions  
Ship wake detection is a powerful tool that can be used to effectively enhance existing hull detection methods. In fact, it has been proven that precious information such as  
Figure 6: Year-by-year timeline of referenced publications on traditional and DL-based wake detection. Updated up to April 2024.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Traditional** & **Deep Learning** \\ \hline
**SAR** & 29 & 5 \\ \hline
**Optical** & 3 & 4 \\ \hline
**TOTAL** & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of referenced publications proposing and testing solutions for wake detection.  
heading, speed, and even hull shape and dimensions can be reliably extracted from the appearance of ship wakes in spaceborne remote sensing imagery. Both optical and SAR applications were explored in this work, highlighting their differences, as well as their strengths and weaknesses.  
The field has evolved significantly in the past decades, transitioning from traditional linear feature extraction methods to advanced DL-based approaches. While effective in detecting basic linear features, traditional methods based on RT and Hough transforms are limited by high false alarm rates and sensitivity to sea clutter. Applications based on other concepts have also been developed and presented in this overview, proving how much exploration has been carried out on the topic.  
Recently, DL-based applications have demonstrated superior performance in detecting complex wake patterns under varying environmental conditions. The integration of attention mechanisms and multi-task learning has further enhanced detection accuracy. The generation of synthetic data has proven crucial in overcoming the scarcity of annotated datasets, enabling DL models to generalize well to real-world conditions. Lightweight DL models, designed for embedded systems, offer promising solutions for real-time maritime surveillance and security applications.  
To advance the field of ship wake detection, several key areas require further research and development. The creation of standardized benchmark datasets for wake detection would facilitate the comparison of different models and foster collaboration among researchers. Developing more sophisticated data augmentation techniques that fit the characteristics of the respective types of satellite imagery, including better simulations of diverse maritime conditions, could enhance the training of DL models. Improving the interpretability of DL models and their robustness to adversarial conditions is essential for reliable real-world applications. Furthermore, combining wake detection models with other maritime surveillance systems, such as information from the AIS, could provide a more comprehensive solution for monitoring and tracking vessels. Finally, the inclusion of information regarding sea characterization and the sources of sea clutter could offer interesting solutions to reduce false positives.  
Conceptualization, A.M. and M.D.G. and A.R.; writing--original draft, A.M.; writing--review and editing, A.M. and M.D.G.; visualization, A.M.; supervision, M.D.G. and A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like acknowledge the Department of Industrial Engineering of the University of Naples Federico II for its notable and continuous support.  
The authors declare no conflicts of interest.