# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### DL-Based Applications for Optical Imagery
The authors focused on imagery from Sentinel-2, considering B2 (blue), B3 (green), B4 (red), and B8 (near infrared) bands, which are all at 10 m. These images were gathered into a dataset, which was called the Ship Wake Dataset (SWD), containing 766 examples including single and multiple wakes, as well as negative examples with no wakes, all validated using AIS data. [PERSON] and [PERSON] [13] approached the problem of low data availability using simulated data to enhance their dataset, focusing on Kelvin wake detection in optical imagery. In particular, they employed a point source perturbation, simulating a specific motorboat in a fixed velocity range, coupled with sea surface slope probability density functions. With this approach, they paid particular attention to the specular reflections of sunlight on the wake regions, which are crucial for Kelvin wake identification in optical imagery, and to accurately simulating the reflectance for each pixel. They populated their dataset, including examples of different illumination and observation geometries, by simulating different Sun zenith angles and satellite zenith angles. These synthetic examples enhanced a dataset composed of 2124 wake samples from Gaofen-1 2 m resolution panchromatic imagery and 8 m resolution multi-spectral imagery. The authors then adopted the GoogLeNet architecture, built on Inception modules [89], which substitutes dense layers with sparse ones, thus significantly reducing the computational efficiency. Their general framework employs pre-processing of the input image, followed by clipping it into overlapping sub-images, which are then classified as either Kelvin wakes or natural surfaces. The clips containing wakes are then merged to highlight the areas covered by wakes. This framework successfully turns a detection task into a much simpler classification task, with an process much akin to classic two-stage detectors using region proposals. The authors also tested their methods by removing wake regions distant from ship hulls, which were manually logged, and labeling ships that did not produce Kelvin wakes as true negatives. The results presented an increase in precision and a slight decrease in recall, as many false positives occurred in areas far away from any identified ships. One final output of interest comes from their study of multi-spectral imagery and the possibility of enhancing the resolution of color and infrared bands using pan-sharpening. However, they noted that the original panchromatic images yielded the most efficient detection outcomes, indicating that high-resolution spatial details are the most critical for identifying Kelvin wakes. In 2023, [PERSON] et al. [64] developed and validated a novel DL approach for detecting ship wake components in electro-optical satellite imagery based on the detection of the key-points of wake components. By employing a transfer-learning procedure to fine-tune ImageNet weights, the authors were able to train a lightweight model based on the EfficientNet architecture [90] with a smaller custom dataset made of Sentinel-2 multi-spectral images and corresponding AIS data. Their method demonstrated robustness across different spectral bands and a heading accuracy under 10\({}^{\circ}\). [PERSON] et al. [64] demonstrated the model's effectiveness on lower-resolution Landsat-9 images, confirming its generalizability. The authors also noted that their approach should be applicable to SAR data, advising the use of ensemble or cascade methods to address false positives, and noted that denoising techniques should be thoroughly investigated. A very recent contribution came from [PERSON] and [PERSON] [11], who tackled the challenge of Kelvin wake detection in large-scale, high-resolution optical imagery. Recognizing the scarcity and limited diversity of real Kelvin wake samples, they proposed a method that utilized simulated Kelvin wakes to augment their training dataset.