# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Introduction to DL-Based Methods for Wake Detection  
While applications based on the RT are extremely common, it is known that they are limited to small images, often centered around the vessel, and work best with low-wind conditions. Other traditional applications of wake detection from airborne and space-borne remote sensing imagery, while based on different processes, generally have issues with different working conditions (i.e., hull characteristics, hull speed, wind conditions, illumination conditions, etc.) and with the presence of too much sea clutter. DL-based applications have shown the ability to overcome these limitations. It is not a surprise, then, that research on wake detection has moved onto DL-based solutions, empowered by the ever-increasing availability of high-quality spaceborne remote sensing data. Object detection is the task of detecting single or multiple instances of certain objects of a certain class (such as cars or buildings) in digital images and videos. Object detection in spaceborne remote sensing imagery has its own specific set of challenges, related to very high background-to-foreground ratios, and more specific characteristics related to the respective sensing platforms [57]. When focusing on ship wake detection, as previously explained, one should remember that ship wakes appear differently in optical and SAR images. In both cases, successful detection depends on both spatial and spectral capabilities. One important thing to note about DL-based applications is that they require large datasets for training, validation, and testing purposes [58]. Not only do the data need to be numerous, but they need to be of high quality (relative to the application of interest) and correctly labeled. In the recent decade, the need for high-quality data has resulted in the generation of several benchmark datasets. Common examples of these are COCO (Common Objects in Context) [59] and ImageNet [60]. In a field that progresses as rapidly as AI, these benchmark datasets provide stable ground for comparison between models, which is precious to researchers and developers alike. Moreover, pre-trained weights for these benchmark datasets are available for most common state-of-the-art architectures, and can represent a great starting point for more specific applications through the application of transfer learning. Although simple datasets of ship wakes have been collected for both SAR [8; 61; 62] and multispectral spaceborne images [10; 63; 64; 13], currently there is no benchmark dataset for wake detection. Information on the few high-quality freely available ship wake datasets is compiled in Table 1, including information on their imagery and corresponding links. One notable case from the abovementioned table is the SynthWakeSAR dataset [66] by [PERSON] and [PERSON], which was developed in terms of ship classification based on synthetic SAR images of wakes. While this work is not further explored in this review, as it is not strictly related to wake detection, it is a notable reference regarding the possibility of extracting vessel information directly from the appearance of their wake, without the need of AIS data. While these datasets exist, new publications on novel wake detection methods are almost always accompanied by their own proprietary datasets with specifically structured annotations. In this sense, it is hard to separate a DL-based wake detection application from its accompanying dataset. Therefore, the following subsections will also contain information on the datasets gathered by the respective authors and the types of annotations they feature. It is important to note that dealing with datasets of satellite imagery poses more limitations than natural images like those from the COCO and ImageNet datasets. For example, when applying data augmentation to SAR imagery, it is important to keep in mind that some standard operations like simple rotations and flips may not have physical significance and could thus introduce errors into the learning process if applied without careful consideration.