# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 1 Introduction  
Ship detection is a topic of great significance in the context of both civil and military applications, which stems from its utility in enhancing maritime security, safety, and environmental protection. A critical challenge for these systems is posed by \"dark\" ships, which are defined as vessels that do not broadcast their location or identification through the automatic identification system (AIS). AIS, despite being a pivotal tool for real-time maritime monitoring, is an opt-in system for most ships, with only vessels over 300 tons being required to keep broadcasting from their AIS transmitter [1]. This means that smaller vessels can deliberately turn off their AIS transmitter, making it difficult to track their movements. This creates a pressing need for alternative methods to monitor and detect ships, especially those engaged in illegal activities like smuggling, unauthorized fishing, and bypassing environmental regulations. Moving ships generate a wake that can highlight their trajectory for tens of kilometers [2]. Theoretically, if one had a complete model of the wake characteristics and their generation mechanism, accompanied by full knowledge of significant local meteo-marine phenomena, the appearance of wake in remote sensing imagery could be used to discern not only the position of the vessel, but also its size, velocity, and heading [3; 4; 5]. This is because the features of the wake of a ship are highly dependent on the previously listed variables [2]. Furthermore, ship detection from spaceborne remote sensing is intrinsically limited by the resolution of the sensors, which greatly reduces the potential for detection of small vessels. In this sense, wake detection provides another avenue for small ship detection, since some structures of the wake offer significantly larger features than the hull itself. Thus, the field of ship wake detection has attracted a wide range of researchers and practitioners from various disciplines, including remote sensing, maritime surveillance,environmental science, and computer science. This interdisciplinary interest underscores the complexity and importance of accurately detecting ships in different conditions and for various applications, and the utility that wake detection can offer for surveillance and security. The methodologies for ship wake detection have evolved from traditional approaches to advanced deep learning (DL) techniques. The methods traditionally used to tackle the problem were based on the application of transforms like Radon and Hough, which served to highlight linear features in the imagery [6; 7]. The transformed data would then be subjected to threshold-based techniques like constant false alarm rate (CFAR). Other approaches required manual feature extraction, following hierarchical paradigms involving the shape, texture, local binary pattern (LBP), and histogram of oriented gradients (HOG) [8]. With the development of DL in recent years, convolutional neural networks (CNNs) have seen an explosive rise in popularity for computer vision (CV) tasks requiring image-based feature extraction [9]. Initial experiments on the application of DL-based detection or segmentation models to the problem in question resulted in an improved detection accuracy of ships and their wakes in complex environmental conditions. However, the most recent developments have involved the application of more task-specific DL models, which often include ideas from traditional approaches. One example of this is the inclusion of a Radon transform (RT) in the architecture of DL detection models [10]. One issue with DL-based applications is that they require large amounts of carefully curated data. For relatively niche applications such as wake detection, data availability can be an issue when it comes to training and testing the neural networks. Thus, most research on DL-based wake detection is accompanied by the development of relevant datasets.